This comprehensive analysis examines the emerging landscape of privacy budgets within the context of ad and tracker blocking technologies, revealing a fundamental tension between preserving targeted advertising ecosystems and protecting user privacy. As privacy regulations intensify globally, with 95% of marketing professionals expecting continued privacy legislation and signal loss, the advertising industry faces unprecedented pressure to develop mechanisms that balance economic viability with meaningful privacy protections. Privacy budgets represent one proposed technical solution to constrain fingerprinting and covert tracking methods, yet their implementation faces substantial technical challenges, limited industry adoption, and fundamental questions about whether they truly serve user privacy or merely create the appearance of protection while enabling new forms of surveillance.
The Architecture of Tracking and the Rise of Privacy Concerns in Digital Advertising
The digital advertising ecosystem has evolved into one of the most sophisticated tracking infrastructures ever constructed, fundamentally reshaping how companies understand, target, and monetize consumer attention. Online advertising has grown to capture more than half of all advertising expenditures in the United States, with the industry’s economic model built upon the ability to determine in real time what advertisement to show to individual consumers by targeting advertising messages to the context of webpages and search queries, as well as targeting based on individual user behaviors. This behavioral targeting, made possible through online tracking and behavioral profiling, has been heralded by the advertising industry as a “win-win” for all stakeholders—publishers, advertisers, consumers, and data intermediaries such as ad networks—by reducing less efficient untargeted advertising and providing better experiences through reduced exposure to irrelevant ads.
However, this economic model has generated profound skepticism and resistance among consumers and regulators alike. The large number of online ads users receive daily has led to frequent criticisms against online advertising for being intrusive and invasive, and as a potential enabler of market manipulation. More critically, due to repeated scandals associated with the handling of personal data by online advertising firms, concerns have kept growing over the risks users face from having their data continuously mined and analyzed by companies they may not even know, and in ways they are often not aware of. This awareness has sparked significant consumer activism through the adoption of privacy-enhancing technologies, with 40% of U.S. respondents in a 2020 survey reporting use of some type of ad-blocking software.
The advertising industry views ad-blockers and anti-trackers with substantial concern. Recent research has estimated that ad-blocking poses a substantial threat to the ad-supported web, with publishers losing an estimated $54 billion annually to ad blockers, representing around 8% of total global ad spend. Between Q4 2021 and Q2 2023, ad blocking adoption grew 11%, reaching a total of 912 million users globally, with mobile ad blocking claiming over 495 million estimated monthly active users. This landscape of adversarial engagement between tracking mechanisms and privacy protection tools has created an urgent industry need for technical solutions that can satisfy both economic requirements and privacy concerns.
Privacy Budgets: Technical Foundation and Conceptual Framework
The Privacy Budget proposal emerged from Google as part of its broader Privacy Sandbox initiative, designed to address what is recognized as a major threat to user privacy: fingerprinting. Unlike “stateful” tracking mechanisms like cookies, which depend on explicit cooperation from a browser, fingerprinting depends on measuring stable characteristics of a given browser such as operating system, version, language, screen size, installed plugins, and numerous other properties. These are properties that are difficult or impossible for browsers to control, allowing for tracking without active browser support. Although any given characteristic may be shared by many browsers, the combination of many such characteristics can be unique or nearly unique, enabling effective cross-site tracking that bypasses traditional cookie controls.
The Privacy Budget mechanism suggests a limit to the amount of individual user data that can be exposed to sites, so that in total it is insufficient to track and identify individuals. This requires quantifying how much users share with third-parties through various methods including k-anonymity, entropy, and differential privacy. K-anonymity represents a property possessed by some anonymized data where “k” is the number of other users with identical information; entropy represents an information theory measure that applies to the level of uncertainty inherent to the possible limit of data; and differential privacy is a system designed to ensure that no individual data can be determined in a set of aggregated data. The maximum tolerance for an amount of information revealed about each user is termed the privacy budget, with the principle that the fewer fingerprinting surfaces available to a site and the lower the granularity of information revealed, the lower the possibility for identification of any single user.
Implementation of the Privacy Budget proposal would theoretically work by estimating the amount of information exposed by fingerprinting surfaces—such as HTTP headers, canvas fingerprinting, or browser properties—and then capping the total amount of information available to any given site. When a site exceeded the cap, it would get an uninformative answer such as an imprecise or standardized value. If successful, this would limit, though not eliminate, fingerprinting by those sites. The proposal suggests that above a set data threshold, the budget could be meaningfully enforced by the browser through several mechanisms: API calls which violate the budget could cause an error; if possible, API calls could be replaced with a privacy-preserving call which returns noised results or generic results not tied to a single user; or storage and network requests could be declined, so that the site cannot exfiltrate new information.
Critical Technical Challenges and Feasibility Concerns
Despite the conceptual appeal of a privacy budget approach, substantial technical and practical challenges have emerged that call its feasibility into question. Mozilla and other privacy researchers have identified numerous critical issues with the Privacy Budget proposal that suggest it may not be practical for real-world deployment.
The first fundamental challenge concerns the difficulty of accurately estimating the amount of information revealed by individual fingerprinting surfaces. The entropy or information-theoretic value of a surface varies substantially depending on the distribution of that property across the user population. For instance, learning that someone uses the Chrome browser is relatively uninformative because Chrome has many users, whereas learning that someone uses Firefox Nightly is highly identifying because there are comparatively few Nightly users. Any system attempting to assign a uniform privacy cost to a particular fingerprinting surface must make assumptions about population distribution that will inevitably be inaccurate. This problem becomes even more severe when considering that different fingerprinting surfaces are not independent from each other—screen width and screen height are highly correlated, operating system version is correlated with browser version, and many other dependencies exist throughout the data that characterizes a browser.
The aggregation problem presents perhaps the most intractable challenge. Even if browsers could accurately estimate the information content of individual fingerprinting surfaces, aggregating these estimates to determine whether a combination of queries exceeds a privacy budget is mathematically problematic. Because the queries are not independent and values are often correlated, simply adding up each query produces misleading results about the true information revealed. A site might work with common browsers but then fail with unusual configurations where each surface carries more information and thus the budget is exceeded more quickly. This creates unpredictable, non-deterministic behavior where some users will experience site breakage and others will not, depending on factors such as their specific system configuration.
The enforcement mechanism itself generates significant practical complications. Websites already query a large number of potential fingerprinting surfaces, often leaving very little—if any—headroom before exceeding a reasonable privacy budget. The unpredictable nature of budget exhaustion could cause widespread site breakage, as websites would exceed the budget and then become unable to make essential API calls. This would be particularly problematic because the order in which the budget is used is nondeterministic and depends on factors such as network performance of various sites and the execution order of JavaScript, meaning that sites might work fine one moment and break inexplicably the next for reasons entirely outside their control.
An especially troubling issue concerns what researchers call the “usability for tracking” problem. The privacy budget mechanism itself can be used for cross-site tracking by measuring which pieces of the budget have been used by a given domain. An attacker could randomly query a set number of fingerprinting surfaces across multiple sites, exhausting the budget in a distinct pattern for each user. On a subsequent visit to a different site controlled by the attacker, the attacker could again query surfaces and determine which ones succeed or fail, thereby identifying whether this is a user they have encountered before based on which parts of the budget have already been consumed. This transforms the privacy budget itself into a tracking mechanism, defeating its intended purpose.
Fingerprinting as the Core Privacy Vulnerability
To fully understand why privacy budgets were proposed and why their limitations matter so profoundly, it is essential to recognize fingerprinting as representing a fundamental vulnerability in web architecture. Fingerprinting enables tracking users across sites without relying on cookies or other storage mechanisms that browsers can control. Instead, it exploits the inherent diversity of browser configurations and system properties to create stable identifiers that persist across browsing sessions and survive cookie clearing or private browsing modes.
The most concerning use of fingerprinting is for cross-site tracking where many sites are integrated with the same tracking infrastructure and can correlate fingerprints across different domains to build comprehensive profiles of user behavior without users’ awareness or consent. This represents a qualitative shift in tracking capability compared to cookie-based tracking because it is largely invisible to users and offers few practical opportunities for consumer control or browser-based mitigation.
Researchers have documented numerous fingerprinting techniques that can identify browsers. Canvas fingerprinting exploits the fact that browsers render text and graphics slightly differently, creating unique “fingerprints” when the same drawing command is executed across different systems. Font enumeration extracts information about installed fonts, which varies between systems. Audio context fingerprinting uses properties of audio processing to create identifying signatures. WebGL fingerprinting exploits graphics rendering differences. Timing attacks measure subtle variations in how long operations take, with high-resolution timing APIs creating precise fingerprints. The combination of even a modest number of these surfaces can create identifiers sufficiently unique to enable individual-level tracking.
Browser vendors have implemented various anti-fingerprinting techniques with limited success. Some browsers randomize or fuzz the responses from APIs to make them less granular and thus less identifying. Safari presents a simplified version of system configuration for this reason. Firefox attempts to reduce fingerprinting surface through User-Agent reduction and other measures. However, these defenses remain imperfect, and the fundamental problem persists: the web platform exposes numerous APIs that provide information useful for legitimate purposes but which collectively enable fingerprinting.
Alternative Privacy-Enhancing Technologies and Mechanisms
Recognizing both the limitations of Privacy Budgets and the inadequacy of simple cookie blocking, the industry and privacy advocates have proposed and developed numerous alternative privacy-enhancing technologies (PETs) designed to preserve user privacy while enabling continued advertising functionality. These technologies represent fundamentally different approaches to the privacy-tracking tradeoff and collectively suggest the direction the industry may take as third-party cookies face deprecation.
Federated Learning represents one of the most promising approaches, offering a decentralized machine learning framework where models are trained collaboratively across multiple data sources without centralizing the data itself. Unlike traditional machine learning, which requires vast amounts of sensitive information to be stored and processed on central servers, federated learning ensures that data remains localized on user devices or institutional servers, with only model updates—mathematical representations of patterns within the data—being shared. For advertising and marketing industries, federated learning allows advertisers to tap into valuable insights such as user preferences, browsing behavior, and purchase patterns without exposing or transferring raw data. Horizontal federated learning enables multiple entities with datasets sharing similar feature sets but different user bases to collaboratively train models reflecting broader behavioral trends. Vertical federated learning bridges data silos with complementary features, unlocking new avenues for customer understanding across parties that traditionally hesitated to share data due to competitive constraints.
Differential Privacy represents another crucial privacy-preserving technology that has been implemented by Microsoft, Apple, Google, and other major technology companies. Differential privacy is a statistical technique that allows companies to share aggregate data about user habits while protecting individual privacy by injecting random data into datasets before sharing. Before data is sent to a server to be anonymized, a differential privacy algorithm adds random noise into the original dataset. The inclusion of random data means that advertisers get datasets that have been masked and therefore are not quite exact. An advertiser viewing differential privacy data might know that 150 out of 200 people saw a Facebook ad and clicked through to its site, but not which specific 150 people, providing plausible deniability because it becomes virtually impossible to identify specific individuals with full certainty. There is a definite tradeoff between privacy and accuracy as advertisers do not get the full picture of how people respond to campaigns, but this sacrifice appears acceptable to many advertisers compared to the privacy risks of individual-level tracking.
Contextual targeting has emerged as a practical and increasingly viable alternative to behavioral targeting that respects user privacy while maintaining advertising effectiveness. Contextual targeting analyzes content using artificial intelligence technologies such as natural language processing, machine learning, and image and video recognition to assess text, images, page structure, and other content elements, then serves relevant ads. Contextual targeting does not rely on personal data about users, making it unlikely to be impacted by privacy concerns, and has evolved from being considered less effective than behavioral targeting to becoming a competitive approach as AI capabilities have improved. If a user is reading a blog about gardening, they may see ads for gardening tools, without the advertiser needing to know anything about that individual’s past browsing history or personal characteristics.
Privacy-preserving identity solutions and first-party data collection represent crucial components of the emerging privacy-conscious advertising infrastructure. With 71% of brands, agencies and publishers expected to increase their first-party datasets, nearly twice the rate of just two years ago, first-party data has become central to marketing strategy. First-party data refers to data a company collects directly from customers and audiences on its own channels through customer interactions, website visits, transactions, and other direct engagements. This data is viewed as the most valuable type for businesses because it comes straight from the source, making it accurate and reliable, and collecting and using first-party data responsibly helps build customer trust while achieving compliance with privacy regulations.
Encrypted advertising approaches and trusted execution environments (TEEs) offer additional technical solutions to the privacy-tracking tension. Google has implemented TEEs in its ad platform to enhance privacy and data protection while providing enhanced insights to users of their ad platform through what Google calls “confidential matching,” where users of Google’s ad platform upload first-party audience lists containing sensitive information such as email addresses and contact information, with Google’s ad service stripping unused identifiers and outputting a list of matched Google users. Importantly, the use of TEEs means that processing of these uploaded audience lists happens in such a way that no one, not even Google, can access the data being processed. By implementing this by default at no additional cost, Google improves its product, enhances security, and delivers clear benefits to end-users.
The Adnostic architecture represents an early research-based approach to privacy-preserving targeted advertising, with profiling conducted on the user’s browser by processing the browser’s history database, with results kept in the browser and used to select ads to display on the page. If the user does not click on the ad, the selection is never communicated outside the browser and consequently no information about the user’s behavior is leaked. Users see ads relevant to their interests, but user information is not leaked to the outside world. Cryptographic techniques ensure accurate accounting between advertisers, publishers, and ad-networks without compromising user privacy, representing a complementary approach that ad-networks could offer alongside user-tracking based advertising services.
The Ad-Blocking Phenomenon and User-Driven Privacy Protection
As technical solutions to privacy concerns have progressed haltingly through industry committees, millions of users have taken matters into their own hands through ad-blocking and tracker-blocking technologies. This grassroots adoption of privacy-protective tools represents perhaps the most direct manifestation of consumer dissatisfaction with current advertising practices and the inadequacy of existing privacy protections. Understanding ad-blocking adoption patterns and user motivations reveals essential truths about the current state of the privacy-advertising relationship and consumer expectations for the digital environment.
Approximately 32.8% of worldwide internet users use ad blockers at least sometimes when online, with younger consumers significantly more likely to adopt the technology than older cohorts. Among those who use ad blockers, 25- to 34-year-olds take the top spot, with 36.9% of men in this age group using ad-blocking software compared to 31.6% of women in the same age group. In the United States specifically, 31% of adult consumers reported using an ad blocker to protect their privacy as of March 2023, with baby boomers (31%) being more likely than Gen Zers (27%) to use the technology for privacy reasons.
The motivations behind ad-blocker adoption reveal critical dissatisfaction with current advertising practices. A 2024 report found that 63.2% of internet users use ad blockers because they think ads are excessive, 53.4% said they get in the way and obstruct content, and 40.3% are motivated by data privacy concerns. Users report that ad-blockers address privacy concerns by keeping advertising technology from gathering personal data, giving internet users more control over who sees their online behavior. Some trackers also suffer from what researchers at NYU Tandon School of Engineering discovered: studies analyzing over 1,200 advertisements across the United States and Germany found that users of Adblock Plus’s “Acceptable Ads” feature encountered 13.6% more problematic advertisements compared to users browsing without any ad-blocking software. This finding challenges the widely held belief that such privacy tools uniformly improve the online experience, revealing that different ad exchanges behave differently when serving ads to users with ad blockers enabled, with some potentially detecting the presence of privacy-preserving extensions and intentionally targeting their users with problematic content.
The economic impact of ad-blocking on publishers and platforms has been substantial, driving industry responses ranging from technical measures to policy approaches. With ad blockers estimated to cost about $54 billion in lost ad revenue in 2024, representing 8% of global ad spend, publishers have deployed increasingly sophisticated strategies to detect and respond to ad-blocking software. YouTube has been leading the charge against video ad blockers, testing ways to warn viewers with ad blocking extensions enabled that they will need to disable their ad blocker, whitelist YouTube, or be restricted to watching only three videos. Audio streamers like Spotify and Pandora have tested and implemented anti-ad blocking technology since 2018. Arc Browser by The Browser Company comes with ad blocking built in, though users can disable that feature. This escalating arms race between ad-blocking technology and platform defenses reflects a fundamental disagreement about the appropriate balance between user control and platform monetization.
Regulatory Landscape and Privacy Legislation Driving Industry Change
The intensity of regulatory action across jurisdictions globally has created perhaps the most powerful force reshaping advertising practices and spurring industry adoption of privacy-protective approaches. Rather than relying entirely on technical innovations or voluntary industry standards, regulators have implemented or are in the process of implementing comprehensive privacy frameworks that impose substantial legal obligations on advertising technology companies and force them to adopt privacy-by-design approaches.
The European Union’s General Data Protection Regulation (GDPR) has established the most comprehensive privacy regime globally, requiring that companies obtain explicit consent before collecting personal data and granting users the right to access, correct, or delete their data. For ad tech companies, the GDPR has far-reaching consequences by limiting how companies can collect and process data, particularly third-party data, and mandating implementation of stringent security measures to protect that data. The GDPR’s implementation beginning in 2018 created widespread industry concern and forced substantial changes to advertising practices, with fines reaching up to 20,000,000 EUR or 4% of total worldwide annual turnover (whichever is higher) for non-compliance.
The California Consumer Privacy Act (CCPA), which came into effect in 2020, provides California’s equivalent to the GDPR, giving consumers the right to access, delete, and opt-out of the sale of their personal data. While less stringent than the GDPR in certain areas, the CCPA still significantly impacts how companies operate, particularly in the ad tech and media sectors. The CPRA (California Privacy Rights Act) amendments have tightened these regulations further. The United States has moved toward a patchwork of state-level privacy laws rather than comprehensive federal privacy legislation, with multiple states including Connecticut, Colorado, Oregon, Montana, Virginia, and Kentucky enacting comprehensive privacy frameworks with varying requirements. These state laws have gradually expanded applicability thresholds, expanded definitions of sensitive data, established heightened obligations for social media platforms, brought nonprofit organizations within the scope of privacy laws, removed exemptions for certain financial institutions, and enhanced protections for minors.
The Children’s Online Privacy Protection Act (COPPA) represents a particularly restrictive regulatory framework, designed to protect the privacy of children under the age of thirteen by imposing strict requirements on websites and apps collecting personal information from children, including obtaining verifiable parental consent before data collection. For ad tech companies, this means implementing systems to verify users’ ages and ensure they are not collecting or targeting children under thirteen. Europe’s Digital Services Act (DSA) and Digital Markets Act (DMA), expected to significantly reshape the ad tech landscape, aim to curb harmful online content, enhance user safety, and prevent anti-competitive practices, impacting how digital platforms handle user data, deliver ads, and ensure transparency in their operations.
This regulatory momentum has created what researchers at the IAB call a “privacy-by-design ecosystem” that is reshaping the entire advertising industry. According to the IAB’s “State of Data 2024” report, 95% of surveyed marketing industry professionals expect continued privacy legislation and signal loss in the future, making a privacy-first approach increasingly urgent. The report found that 82% of those surveyed reported that the makeup of their organizations has been impacted by signal loss, with approximately three-quarters expecting their ability to collect consumer data will continue to degrade. This has driven substantial strategic shifts, with 90% of ad buyers shifting personalization tactics as a result of increased privacy legislation and signal loss, and ad budgets increasingly allocated to channels that can leverage first-party data, such as connected TV, retail media and social media.
Google’s Privacy Sandbox Initiative and the Complexity of Industry Solutions
Google’s Privacy Sandbox represents perhaps the most comprehensive attempt to create an industry-wide alternative to third-party cookies while maintaining targeted advertising capabilities. Announced in 2019, the initiative aimed to develop new ways to strengthen online privacy while ensuring a sustainable, ad-supported internet through privacy-enhancing approaches and new solutions built using Privacy Sandbox APIs. The project included numerous proposed technologies designed to address specific advertising use cases while maintaining privacy protections.
The Privacy Sandbox originally included dozens of proposed technologies covering multiple advertising functions. The Attribution Reporting API aimed to measure the effectiveness of advertising campaigns without revealing user identity, allowing advertisers to know if an ad led to a conversion without knowing the specifics of who performed the action. The FLEDGE (First Locally-Executed Decision over Groups Experiment) API offered a method for executing ad biddings and targeting directly in the user’s browser, instead of on remote servers, with the user’s browser storing interests and limiting information that can be inferred from browsing history. The Topics API relied on machine learning to infer a person’s interests from the names of recently visited sites, enabling targeting the right audience for specific topics without compromising privacy. CHIPS (Cookies Having Independent Partitioned State) allowed developers to opt a cookie into partitioned storage with a separate cookie jar per top-level site. Federated Credential Management (FedCM) supported federated identity without sharing the user’s email address or other identifying information with third-party services unless the user explicitly agreed.
However, the Privacy Sandbox initiative has struggled with industry adoption and regulatory acceptance. In April 2025, Google announced a significant reversal, confirming that Chrome would not be phasing out third-party cookies after all. In October 2025, Google further announced that it was retiring numerous Privacy Sandbox technologies entirely, including the Attribution Reporting API, IP Protection, On-Device Personalization, Private Aggregation, Protected Audience, Related Website Sets, SelectURL, SDK Runtime, and Topics, citing “low levels of adoption” and acknowledging feedback about their expected value. This retreat represents a dramatic acknowledgment that the industry’s privacy solutions had failed to achieve meaningful adoption or support.
Google’s decision reflected multiple constraints and failures. Tests revealed measurable drops in monetization and advertising performance that were difficult for the company to accept given its business model’s dependence on targeted advertising. Regulators watched the Privacy Sandbox closely, with the UK’s Competition and Markets Authority raising questions about whether Privacy Sandbox could actually increase Google’s control rather than protect internet users. The combination of technical infeasibility for many proposed approaches, limited industry enthusiasm, and regulatory skepticism created an environment where Google determined that abandoning the initiative was preferable to continuing with solutions that were technically flawed, industry-rejected, and potentially subject to regulatory action.
The Third-Party Cookie Debate and Industry Uncertainty
The saga of third-party cookie deprecation illustrates the difficulty of coordinating industry-wide technological transition when economic incentives and regulatory pressures push in different directions. Google’s announcement and reversal on third-party cookies created years of uncertainty in the advertising industry, forcing marketers to invest in preparation for a cookieless future while subsequently discovering that those investments might have been premature.
Third-party cookies are cookies set by websites different from the site a user is visiting, enabling cross-site tracking and targeted advertising. These cookies have historically been the foundational technology enabling programmatic advertising by allowing tracking pixels and analytics tags to measure user behavior across multiple sites and link that behavior to individual identifiers. However, third-party cookies are increasingly blocked by default in major browsers other than Chrome, with Safari and Firefox already blocking them by default. This means that for significant portions of the internet user population, the cookieless world is already a reality, even if Google Chrome maintains support.
The uncertainty surrounding cookie deprecation created a complex planning environment for advertisers. When Google first announced its intention to phase out third-party cookies in 2020, setting a two-year deadline, the industry began preparing for a major transition. By 2021 and 2022, the deadline had already slipped as advertisers and regulators voiced concerns. In 2023, Privacy Sandbox testing ramped up but adoption remained limited. In July 2024, Google surprised the industry with a U-turn, delaying cookie deprecation indefinitely. Finally, in April 2025, the Chrome browser confirmed that third-party cookies would remain in place.
This extended period of uncertainty had paradoxical effects. Initial research by Acquia found that 88% of marketers said that first-party data is more important to organizations than ever, representing a genuine strategic shift driven by recognition that reliance on third-party cookies was becoming untenable. However, Duke University research revealed that while 58.3% of US CMOs created a stronger data strategy to capture better information in response to the initial cookie deprecation announcement in February 2022, this proportion had decreased to 41.9% by September 2024 as the threat of deprecation became uncertain. This pattern illustrates how regulatory or platform uncertainty can undermine the adoption of genuinely beneficial practices when companies are unsure whether the investment will be necessary.
Despite Google’s reversal on cookie deprecation, the pressures that motivated the original deprecation timeline remain in force. Safari and Firefox already block third-party cookies by default, meaning that marketers who rely heavily on third-party cookies are increasingly unable to track users across portions of the web. Privacy regulations continue to tighten globally, with more states implementing privacy laws and existing laws expanding in scope. Consumer privacy concerns persist and, according to recent data from Cisco, 94% of organizations acknowledge that customers will not buy from them if data is not properly protected.
First-Party Data as the Foundation of Privacy-Conscious Marketing
Recognizing that third-party cookies cannot be relied upon indefinitely, the advertising and marketing industries have increasingly focused on first-party data collection and activation as the foundation of sustainable marketing strategy. First-party data refers to data a company collects directly from customers and audiences on its own channels, typically obtained through customer interactions, website visits, transactions, and other direct engagements. This data is viewed as the most valuable type for businesses because it comes straight from the source, making it accurate and reliable, and respects fundamental privacy principles through direct consent and relationship.
The shift toward first-party data strategies has been dramatic. The IAB’s 2024 report found that 71% of brands, agencies and publishers are expected to increase their first-party datasets, nearly twice the rate of just two years ago. The types of first-party data being collected by companies include contact information (84%), device information (81%), transactions (73%), content consumption (70%) and location (69%). This data collection represents a more transparent and consensual approach compared to third-party tracking because it typically involves direct customer relationships and explicit or implicit consent through the act of visiting a website or using an application.
First-party data enables marketers to create highly personalized marketing campaigns addressing specific customer segments with tailored messages and offers. By organizing first-party data in centralized customer relationship management (CRM) systems, marketers can segment audiences based on shared characteristics, create detailed buyer personas representing ideal customers, and craft individualized email campaigns, provide tailored recommendations, or incorporate dynamic website content. This personalization approach can create significantly improved customer engagement and loyalty compared to untargeted or poorly targeted advertising while respecting privacy principles that inform current regulations.
However, the transition to first-party data strategies has created new challenges and limitations. One significant issue concerns interoperability between different first-party data channels. The IAB report found that one challenge marketers face as they focus more on first-party data is a lack of interoperability between key first-party data channels like connected TV, retail media, and social media, making it harder to assess effectiveness. Without the standardized cross-site tracking enabled by third-party cookies, measuring the impact of advertising campaigns across multiple channels becomes substantially more difficult. Marketers lack integrated views of customer journeys and struggle to attribute conversions to specific advertising touchpoints when data remains siloed within individual platforms and properties.
Additionally, measurement challenges have become increasingly acute. Over half (57%) of marketers surveyed believe it will be harder to capture reach and frequency in a privacy-conscious environment, while 73% believe their ability to attribute campaigns, track performance and measure return on investment will be reduced. These measurement difficulties are not merely technical inconveniences; they represent fundamental business challenges that affect marketing budget allocation decisions and the ability to demonstrate marketing efficiency to finance departments and executive leadership.
Privacy-Enhancing Technologies Beyond Privacy Budgets
While Privacy Budgets represent one proposed approach to privacy protection, the broader ecosystem of Privacy-Enhancing Technologies (PETs) encompasses numerous complementary or alternative approaches that address different dimensions of the privacy-tracking tension. Understanding this broader landscape reveals that privacy solutions are increasingly being developed through specialized technical approaches rather than through universal mechanisms like Privacy Budgets.
Differential privacy has emerged as perhaps the most widely adopted privacy-enhancing technology across major technology companies and industry solutions. Apple, Google, Microsoft, and numerous other companies have implemented differential privacy as a core component of their privacy-protective data processing systems. The appeal of differential privacy lies in its mathematical foundation and the formal privacy guarantees it provides. Rather than attempting to prevent data collection entirely, differential privacy allows companies to analyze data and extract patterns while maintaining strong mathematical guarantees that the participation or non-participation of any individual in the dataset cannot be reliably inferred from the output.
Homomorphic encryption and secure multi-party computation represent more specialized but increasingly important PET approaches for scenarios where multiple parties need to collaborate on data analysis without sharing raw data. Homomorphic encryption allows computation to be performed on encrypted data without decryption, enabling analysis of sensitive information without exposing underlying values. Secure multi-party computation enables organizations to compute results across distributed data sources through cryptographic protocols that prevent any single party from learning individual contributions. These approaches are particularly valuable for scenarios where competitors need to collaborate on measurement and analysis while protecting competitive information.
Trusted Execution Environments (TEEs) represent another important PET approach where computation is performed in isolated environments within processors that prevent even device owners or operating systems from accessing the data or computations happening within. Google’s implementation of TEEs in its ad platform for “confidential matching” demonstrates how this technology can enable sensitive data processing while preventing unauthorized access. The key innovation involves processing uploaded audience lists containing personally identifiable information such as email addresses entirely within the TEE, with outputs provided to the advertiser but with no possibility of Google or other parties intercepting the underlying data.
On-device learning and federated learning have become increasingly important as the industry recognizes that certain computations can be performed locally on user devices without transmitting sensitive information to centralized servers. This approach is particularly valuable for machine learning applications where training models on user data locally, with only model updates being aggregated and shared, provides substantially better privacy properties than centralizing data. The efficiency and scalability benefits of edge computing combined with privacy guarantees make federated learning increasingly attractive for advertising use cases where real-time personalization and recommendation require responsive computation.
Anonymization and pseudonymization technologies remain important privacy-protective approaches, though researchers have documented substantial limitations of anonymization techniques. Perfect anonymization of data that is useful for advertising purposes is difficult to achieve because the combination of diverse data points can often be re-identified. Nevertheless, anonymization and pseudonymization still provide valuable privacy protections when applied carefully in combination with other techniques.
The Measurement Crisis and Attribution Challenges
One of the most consequential impacts of the privacy-advertising transition concerns the fundamental difficulty of measuring advertising effectiveness in a privacy-protective manner. As data collection becomes constrained by regulation and privacy protections, advertisers face unprecedented challenges in understanding which advertising efforts drive business results and how to optimize budget allocation across channels and campaigns.
The IAB report found that measurement represents a significant casualty of the changing privacy landscape, with 57% of marketers believing it will be harder to capture reach and frequency while 73% believe their ability to attribute campaigns, track performance and measure return on investment will be reduced. This measurement crisis has multiple dimensions. Attribution modeling becomes difficult when tracking data is incomplete or absent. Actions such as clicking a link or making a purchase may not be tracked if someone has an ad blocker or privacy protections enabled, preventing marketers from connecting these actions back to specific ads. This leaves marketers unable to demonstrate the connection between advertising efforts and business outcomes, a fundamental necessity for optimizing budget allocation and justifying advertising investments to organizational leadership.
The W3C’s work on privacy-preserving attribution through the Attribution API represents an attempt to address this measurement challenge by providing browsers with standardized APIs for measuring advertising effectiveness while limiting data exposure. The Attribution API attempts to provide aggregated, differentially-private conversion measurement that enables advertisers to understand campaign effectiveness without individual-level user tracking. However, even this standards-based approach faces significant challenges, with research on the Attribution API identifying issues with privacy budget management and the possibility of denial-of-service attacks where multiple queriers competing for the same shared privacy budget create practical challenges for utility.
Alternative measurement approaches have emerged to address attribution challenges, including incrementality testing, media mix modeling, and multi-touch attribution. Incrementality testing attempts to measure advertising impact by comparing outcomes in markets where advertising runs with matched control markets where advertising does not run, providing experimental evidence of advertising effectiveness. Media mix modeling employs statistical techniques to estimate the contribution of different marketing channels to business outcomes based on historical data. Multi-touch attribution attempts to distribute credit for conversions across multiple advertising touchpoints in a customer journey. These approaches provide valuable alternatives to pixel-based tracking but require different skills, data sources, and analytical capabilities than traditional tracking-based measurement.
The measurement crisis has sparked industry innovation and alternative approaches. CTV (Connected TV) and DOOH (Digital Out-of-Home) advertising have transformed from awareness-focused channels into performance marketing channels through technological innovations including shoppable ads, dynamic creatives, and enhanced measurement capabilities. As advertisers seek channels where measurement and attribution remain feasible, CTV spend is expected to exceed linear TV by 2027, with programmatic DOOH spending expected to grow to $1.23 billion by 2026.
Industry Responses and the Shift in Organizational Structure
The pressure from privacy legislation, signal loss, and technological change has forced substantial organizational changes within marketing and advertising-focused companies. The IAB report found that 82% of those surveyed reported that the makeup of their organizations has been impacted by signal loss. Large companies have been substantially better positioned than small and medium-sized firms to invest in privacy-protective infrastructure, creating a competitive advantage that may consolidate the advertising industry around larger players with greater resources.
Large companies are investing in identity anonymization training at a rate of 79% compared to 64% for small companies, while 62% of large companies have invested in privacy-preserving technologies compared to just 44% of small companies. Since such tools can cost over $2 million a year, this price point excludes many small firms from access to cutting-edge privacy-protective advertising solutions. This disparity may disadvantage smaller publishers and advertising firms that lack the resources to build or purchase sophisticated privacy-enhancing technology infrastructure.
Advertising budget allocations have shifted substantially in response to privacy limitations. In 2024, 54% of those surveyed indicated they would increase their ad spend on CTV due to privacy legislation and signal loss. Other channels being eyed by marketers include influencer marketing (52% expected to increase spend), paid search (51%), social media (50%), and retail media (47%). These channel shifts reflect movement toward first-party data channels, contextual approaches, and relationships where advertisers maintain direct connections to audiences.
Investments in data infrastructure have become critical competitive factors. With 71% of brands, agencies and publishers expected to increase their first-party datasets and 75% of marketers heavily relying on third-party cookies according to recent surveys, the transition to first-party data and privacy-conscious marketing requires substantial technological investment. Customer data platforms (CDPs) and marketing technology (martech) solutions have proliferated, with the 2024 Marketing Technology Landscape Supergraphic including 14,106 tools, representing a 27.8% increase from the previous year and highlighting the increasing fragmentation of the martech ecosystem. With 28.8% of US ad agencies reporting they use six to seven adtech and martech tools as part of their tech stack, the fragmentation and lack of interoperability has become a significant barrier to overall efficiency and effectiveness.
Problematic Ads and the Hidden Costs of Ad-Blocking
Emerging research has documented an often-overlooked consequence of the privacy-advertising conflict: ad-blocking and privacy protections may inadvertently expose users to more problematic and harmful advertising content while they attempt to protect themselves. This paradox illustrates the complexity of the ecosystem and unintended consequences of technical interventions in adversarial environments.
The NYU Tandon study found that users of Adblock Plus’s “Acceptable Ads” feature encountered 13.6% more problematic advertisements compared to users browsing without any ad-blocking software. The research team developed an automated system using artificial intelligence to identify problematic advertisements at scale across seven categories of concerning content: ads inappropriate for minors (such as alcohol or gambling promotions), offensive or explicit material, deceptive health or financial claims, manipulative design tactics like fake urgency timers, intrusive user experiences, fraudulent schemes, and political content without proper disclosure.
Most troublingly, the study revealed particularly concerning patterns for younger internet users, with nearly 10% of advertisements shown to underage users violating regulations designed to protect minors. This highlights systematic failures in preventing inappropriate advertising from reaching children, precisely the problem that drives many users to adopt ad blockers in the first place. The research revealed that advertising exchanges behave differently when serving ads to users with ad blockers enabled, with some exchanges showing fewer problematic advertisements while existing approved exchanges actually increased their delivery of problematic content to privacy-conscious users.
The implications extend beyond user experience concerns. The researchers warn that differential treatment of ad blocker users by ad exchanges could enable a new form of digital fingerprinting, where privacy-conscious users become identifiable precisely because of their attempts to protect themselves. This creates a “hidden cost” for privacy-aware users who may find themselves targeted with lower-quality or more problematic advertisements as a consequence of their privacy protection efforts. This pattern illustrates how privacy protections and advertising systems can create perverse incentives when the ecosystem develops adversarially, with different parties pursuing conflicting objectives without oversight or coordination.
Regulatory Budgeting Constraints and Organizational Challenges
While privacy budgets represent a technical proposal concerning information exposure limits, a parallel but distinct issue concerns organizational budgets for privacy programs and the resource constraints that prevent many organizations from implementing adequate privacy protections. Research from ISACA and other privacy organizations documents substantial challenges that organizations face in establishing and maintaining adequate privacy programs.
According to ISACA’s 2024 report on “The State of Data Privacy,” 57% of cyber professionals lack confidence in their organization’s privacy team’s ability to ensure data privacy and comply with new regulations. One key reason is the widespread lack of understanding about privacy regulations, with two-thirds (66%) of professionals admitting to this challenge. Among the 1,300 respondents, 43% pointed out budgetary constraints as a significant challenge, with many stating their privacy budget is underfunded. More than half of professionals expect further reductions in privacy budgets for 2024.
These budgetary constraints translate into practical difficulties for IT professionals when establishing data privacy programs. The most commonly cited obstacles include lack of skilled resources (41%), unclear mandate, roles, and responsibilities (39%), limited executive or business support (37%), and insufficient visibility and influence within the organization (37%). These findings suggest that many organizations view privacy programs as compliance obligations rather than strategic investments, allocating insufficient resources to genuinely protect user data and achieve meaningful privacy outcomes.
The challenge is exacerbated by the growing complexity and proliferation of privacy regulations. While GDPR and CCPA established foundational frameworks, the regulatory landscape has become increasingly fragmented with multiple state-level laws in the United States, regional regulations in Europe, and various international requirements creating a patchwork of obligations. Organizations operating across multiple jurisdictions face the challenge of implementing compliant systems and processes that accommodate different regulatory requirements that often conflict or impose overlapping obligations.
Data privacy is historically underfunded regarding company budgets, even as “data privacy” has become a popular topic. Some stakeholders view regulations like the GDPR or CCPA as one-time, check-the-box projects and therefore fail to fund appropriately, though those handling privacy management daily know that this is not the case given the numerous complex privacy regulations that require ongoing engagement. Developing a mature privacy program is crucial to ongoing risk management and compliance, and overlooking data privacy budget limitations can be costly for organizations facing substantial regulatory fines and reputational harm from privacy breaches.
Future Directions and Emerging Solutions
As the limitations of individual technical approaches become apparent, the industry is increasingly pursuing a multi-pronged strategy combining multiple privacy-protective techniques, regulatory compliance mechanisms, and business model innovations. The future of advertising appears to be moving toward a more diversified ecosystem where different channels, technologies, and approaches coexist rather than a single unified solution replacing current practices.
Meta’s introduction of ad-free subscription services for Facebook and Instagram users in Europe represents a significant business model innovation, allowing users to opt out of advertising for a monthly fee. This shift represents a fundamental change in digital advertising by giving consumers more control over their online experience while challenging traditional ad-based revenue models. The implications for UK and global advertisers are substantial, with potential shrinking reach for digital ads, higher costs due to greater competition for limited ad space, increased emphasis on organic content, and ongoing regulatory and privacy considerations.
The advertising industry will likely continue to rely on multiple complementary approaches rather than a single privacy solution. First-party data strategies will remain central, with organizations investing heavily in customer data platforms and capabilities to collect, integrate, and activate first-party data. Privacy-enhancing technologies will proliferate across different use cases and scenarios, with federated learning, differential privacy, and trusted execution environments becoming increasingly standard in marketing technology stacks. Contextual advertising will evolve from a supplementary approach to a primary targeting mechanism as AI capabilities improve. Alternative measurement approaches including incrementality testing and media mix modeling will become more sophisticated and widely deployed. Connected TV, retail media, and other channels where first-party relationships exist will continue to gain advertising budget share.
The regulatory environment will continue to tighten, with more jurisdictions implementing privacy regulations and existing laws expanding in scope and enforcement actions. Organizations that have treated privacy as merely a compliance obligation will face increasing pressure to genuinely embed privacy-protective practices into their core operations. The competitive advantage conferred by sophisticated privacy-protective infrastructure will likely increase, potentially consolidating the advertising industry around larger players with greater resources to invest in privacy-enabling technologies.
Perhaps most significantly, the fundamental tension between targeted advertising and privacy protection will persist without resolution. Privacy budgets, while technically sophisticated and well-intentioned, have proven impractical and insufficient to address the underlying challenges. Alternative approaches including federated learning, differential privacy, and contextual targeting offer meaningful improvements compared to current practices, but involve tradeoffs and limitations that prevent perfect solutions. The future of advertising will likely involve accepting these tradeoffs explicitly rather than seeking a technical silver bullet that perfectly reconciles advertising effectiveness with privacy protection.
Shaping Advertising’s Future with Privacy Budgets
Privacy budgets represent an important but ultimately limited attempt to address one critical dimension of the privacy-tracking tension: the use of fingerprinting and passive data collection to create stable cross-site identifiers. While conceptually appealing, practical implementation challenges including difficulty estimating information content, aggregation problems, non-deterministic behavior, and potential misuse of the budget mechanism itself as a tracking tool have prevented privacy budgets from becoming a viable solution. Google’s decision to retire numerous Privacy Sandbox technologies and reverse its earlier commitment to third-party cookie deprecation reflects the industry’s acknowledgment that these technical solutions, however well-intentioned, have failed to achieve the necessary feasibility and adoption levels.
The broader lesson from the privacy budgets episode concerns the limitations of purely technical approaches to fundamentally economic and social tensions. The conflict between targeted advertising and user privacy reflects different interests and values that cannot be perfectly reconciled through technical innovation alone. Users want control over their personal information and freedom from invasive tracking. Advertisers want access to data enabling effective targeting and demonstrating campaign effectiveness. Publishers want revenue streams supporting content creation. These interests are not fully compatible, and technical solutions that ignore this fundamental tension are unlikely to succeed.
The future of advertising appears to be moving toward a more pragmatic and diverse ecosystem where multiple approaches coexist. First-party data strategies, privacy-enhancing technologies applied to specific use cases, regulatory compliance mechanisms, alternative measurement approaches, and business model innovations including ad-free subscription options will likely all play roles in the evolving advertising landscape. Privacy budgets may continue to be studied and refined in academic contexts, but their role in practical advertising infrastructure seems likely to be minimal compared to more targeted and pragmatic solutions.
For organizations operating in this complex environment, the implications are substantial. Investing in first-party data collection and activation capabilities has become essential for sustainable marketing practice. Adopting privacy-enhancing technologies appropriate to specific use cases rather than seeking universal solutions appears more promising than waiting for industry-wide technical standards. Understanding regulatory obligations and implementing genuine privacy-by-design approaches offers competitive advantages through both compliance assurance and consumer trust. Most fundamentally, recognizing that perfect reconciliation between advertising effectiveness and privacy protection is unlikely enables organizations to make explicit tradeoff decisions aligned with their values and strategic objectives rather than expecting technology alone to solve inherently human conflicts about privacy, targeting, and the appropriate balance between business interests and individual rights.
Protect Your Digital Life with Activate Security
Get 14 powerful security tools in one comprehensive suite. VPN, antivirus, password manager, dark web monitoring, and more.
Get Protected Now
