Advertising technology (adtech) presents a unique opportunity for brands and retailers to collaborate and unlock value from their combined first-party data assets. Privacy regulations revolving around third-party cookie deprecation1 (e.g., the EU General Data Protection Regulation [GDPR],2 the US State of California Consumer Privacy Act [CCPA]3) limit traditional data sharing. However, secure platforms enabled by techniques such as secure multiparty computation (SMPC) allow partners to derive insights and optimize media campaigns without pooling or exposing raw data. By working together to preserve privacy, brands and retailers can strengthen engagement and acquisition capabilities despite losing third-party data signals and identifiers. The future of data-driven marketing requires adaptation to emerging privacy norms while extracting powerful insights from combined—but securely protected—first-party data.
SMPC is a cryptographic technology (and a privacy-enhancing technology, also known as PET) that enables different parties to analyze data jointly without sharing it. The input data originates from different organizations or entities holding separate private data sources. The raw input data remains encrypted during the entire process. SMPC prevents any participating party from accessing or stealing others' data. Parties can collaboratively analyze data and compute results, all while preserving the confidentiality of their proprietary inputs and ensuring full compliance with privacy regulations.
SMPC uses homomorphic encryption and secure multiparty protocols that enable computations to be carried out on encrypted data without ever decrypting it.4 An example of a secure multiparty protocol is a private set intersection (PSI). PSI computes the intersection between private datasets without revealing anything more, whereas homomorphic encryption preserves the structure of the plaintext data and allows for operations such as addition and multiplication to be performed directly on the ciphertext. The computations culminate in the generation of an encrypted outcome. This encrypted output can then be unveiled to all participating parties.
SMPC ensures that no party learns more about the other parties' inputs than what can be inferred from the function's output. To develop an SMPC protocol, the parties must specify what computation they want to perform and who will provide inputs. SMPC algorithms enable joint computations, such as aggregation and machine learning (ML), to extract insights across decentralized data sources.
PETs can be categorized by legacy and emerging technologies, offering a strategic pathway for their application based on specific data privacy scenarios (figure 1).
FIGURE 1—Privacy Obligations and Regulatory Mandates Mapped to Emerging PETs5
Legacy PETs, such as data aggregation and pseudonymization, are still relevant in certain low-dimensional data contexts where the need for customer anonymity outweighs the complexity of the data. These straightforward and well-understood methods provide a baseline for privacy protection.
Emerging PETs are the next evolution of data privacy technologies, offering robust protection for high-dimensional data. These methods are particularly adept at preserving individual privacy in large datasets, allowing for the extraction of valuable insights without compromising sensitive information.
SMPC is gaining significant traction among emerging PETs for several key reasons, as it equips complex collaborative analytics and shared insights across datasets from multiple parties without any central pooling or sharing of raw data. This aligns well with the data privacy regulations to which small and large organizations must adhere. No other PET combines the ability to perform rich, meaningful computations across decentralized parties' data with such strong security models so that all parties can participate safely. This unique blend makes SMPC valuable. The cryptography in SMPC acts as a shield around the source data by keeping the raw, sensitive data encrypted end to end during collaborative computation and analysis between parties. Cryptographic hash functions, such as SHA-256, can generate a unique hash value for an email address. This unique hash value is frequently used as a common identifier to join data. Publishers and advertisers can verify that the analysis of the joint encrypted data was performed correctly and that the encryption ensures no tampering or errors. The verification process for the advertiser (also referred to as brand) involves using the encrypted email address for audience targeting through ad platforms. Once the advertising campaign goes live, the advertiser can see the size of the audience. In another use case of finding the overlap of common users between the publisher and the advertiser, both parties get the count of unique users common to both datasets. Each party always maintains complete control over what data they contribute.
In summary, SMPC facilitates collaborative analytics across decentralized data sources without exposing raw data. As emerging privacy regulations limit traditional data sharing, SMPC allows parties such as brands and publishers to jointly derive insights from combined first-party data while fully protecting sensitive information. Understanding how SMPC allows for secure data collaboration increases the value of reviewing specific use cases, demonstrating its application in advertising technology to drive marketing optimization and performance. It is also worth exploring tangible examples of how SMPC unlocks value for brands, publishers, and consumers even as third-party signals decline.
SMPC Use in Advertising Technology
SMPC has compelling applications within advertising. From optimizing media spend to unraveling customer insights and identifying fraud, SMPC enables secure data alliances as cookie signals decline. There are use cases that showcase the analytical potential unlocked for marketers when sensitive information remains protected through state-of-the-art cryptographic techniques. Whether enhancing campaign efficiency, measuring performance, or employing smarter customer segmentation, previously impossible multiparty computations now have a privacy-first solution via emerging privacy-enhancing technologies such as SMPC.
There are numerous SMPC use cases that can be used to increase value within an enterprise:
- Enhance media spend efficiency and marketing strategy. Optimize media spending, reduce media overlap, and slash waste by understanding which investments drive purchases. Analyze halo and cannibalization trends across multibrand portfolios.6
- Boost measurement and attribution. Utilize SMPC to enable collaborative analysis of attribution windows and optimize return on advertising spend metrics across dimensions, channels, and partners. SMPC can match encrypted impression and conversion data to precisely model conversion latency (the time lag between ad exposure and a user completing a desired action such as a purchase). Tuning this conversion window assignment based on combined data allows for tightened attribution rules so that conversions are credited to the ad touchpoints that truly influenced the consumer's journey. This prevents inflated performance from overcounting conversions with long lag times. With properly assigned conversion credit, media performance analytics such as return on ad spend become much more accurate, justifying continued investment in consistently high-value partnerships. Unlike traditional methods, SMPC attribution analysis provides cross-party consumer pathing insights to optimize channel mix and partner allocations without exposing raw ad impression or sales data, upholding privacy compliance and performance optimization simultaneously.
- Enhance user profile enrichment. SMPC facilitates retailers and consumer brands to jointly analyze encrypted first-party data to unlock deeper insights into customer profiles, preferences, and behaviors without exposing raw information. By comparing purchase patterns, product affinities, marketing channel responsiveness, sociodemographics, and other important attributes across datasets, parties can collaborate to uncover a holistic view of customers and refine segmentation. With SMPC, partners get a better context of shared customers beyond their interactions. The intelligence gathered drives superior personalized engagement, enabling the delivery of relevant product recommendations and promotions based on purchase history. It also enables tailored customer experiences across touchpoints and life cycle stages, helping retain valuable users.
- Reach relevant audiences at scale. SMPC equips valuable data collaborations between enterprises such as a loyalty provider and Connected TV (CTV) media owner. The loyalty provider has access to rich customer insights from purchase history and CRM data. However, it lacks scalable distribution pipes to activate audiences. Meanwhile, major CTV platforms have massive reach and targeting, but less owned data intelligence. By analyzing encrypted viewership and loyalty data, the loyalty provider and CTV media owner can together define high-value audience segments matching engaged customers to receptive CTV exposures. The loyalty provider supplies the data intelligence to help identify valuable audience segments, while the media owner enables reaching those target segments at scale, given its large media inventory and existing ad platforms.
- Conduct incrementality testing. Incrementality testing measures the true impact of marketing efforts by analyzing business performance with and without the campaign to quantify added value. Typically, retailers only see total sales trends, while brands are restricted to campaign-specific data. However, each needs the full counterfactual picture for accurate analysis. SMPC bridges this gap by enabling joint analysis of encrypted sales and marketing datasets across partners, facilitating accurate modeling of incremental lift without exposing raw data. Rather than extrapolating based on fragmented datasets, SMPC powers secure computations on combined assets to statistically isolate the unique value directly driven by specific campaigns, promotions, etc. This collaboration unlocks robust return on investment (ROI) analysis previously infeasible without exposing all sales information and marketing data.
- Detect and prevent ad fraud. Fraud is a significant issue in the digital advertising ecosystem, with advertisers often paying for clicks or impressions that are not from genuine users. SMPC can be used to analyze data from various sources to identify patterns indicative of fraud without compromising the privacy of the data. Different parties, such as ad networks, publishers, and advertisers, can analyze traffic and engagement data collaboratively. SMPC allows them to do this so that no single party can access the complete dataset, thus maintaining data privacy.
- Analyze advertising-driven consumer purchase behavior. Ecommerce platforms and consumer brands have distinct data on purchases: Platforms observe transactions while brands run advertising and observe campaign-exposed sales. However, analyzing these datasets in isolation limits visibility into advertising effectiveness on actual buying conversions. SMPC enables encrypted analysis of campaign impressions and transactions to uncover advertising influence on purchasing while protecting raw data. Homomorphic encryption allows for aggregated computations, for example, calculating lift in total sales among ad-exposed shoppers across the brand and platform’s combined data assets. These privacy-preserving collaborations benefit brands with more robust ROI analyses and inform optimal media investment strategies.
- Employ customer segmentation. Using SMPC, retailers can work with consumer brands to define joint customer segments among their encrypted customer profile data to define subgroup targeting strategies. This process helps uncover patterns and segments in the combined data, such as by identifying customers with a preference for eco-friendly products or those responsive to specific marketing channels. The potential results of this collaboration are multifaceted: The retailer can optimize product placement and personalized promotions, while the brand can fine-tune its marketing campaigns and product development to align with customer preferences.
The range of applications highlighted shows how SMPC can empower key players across advertising to improve campaign performance, audience targeting, and revenue protection. As consumer privacy expectations and regulations rightfully evolve, SMPC provides a crucial framework for advertisers, publishers, retailers, and platforms to continue exchanging insights from first-party data assets without exposing the underlying raw information. As external signals decline, these privacy-safe connections enabled by SMPC will only grow more integral to driving outcomes across the advertising ecosystem.
Case Study
Several real-world examples demonstrate how leading brands are using secure data sharing platforms to solve key business problems. Case studies show major consumer goods enterprises, media firms, and retailers deploying encrypted data collaborations to improve marketing and sales outcomes without compromising on privacy. The case studies also cover key lessons around setting goals, choosing technologies, organizing teams, and evaluating progress from pioneers such as Disney. A study of enterprises Indeed and Disney demonstrates that respecting customer privacy can coexist with business growth. The numbers prove impressive gains, while the specifics equip brands to pursue secure data sharing for themselves. This emphasizes that privacy and profits can align through emerging cryptography to future-proof, responsible, yet results-driven data collaborations even as regulations and consumer scrutiny increase.
Data Partnerships in the Privacy Era: An Indeed and Disney Case Study7
Indeed, a job search platform, used Disney's secure data clean room to find new audiences and increase revenue for its job site without compromising privacy. Disney Advertising’s clean room allows marketers to transact on first-party data in a secure manner, delivers real results for enterprises, and delivers better ad experiences for consumers. To fully harness the potential of emerging privacy-safe platforms such as data clean rooms (also known as data collaboration platforms), adtech leaders now prioritize building scalable data architectures and collaboration frameworks that synthesize diverse inputs and streamline coordinated efforts. Forming strategic partnerships, such as in the case of Disney and Indeed, will be instrumental in modernizing technical infrastructure to facilitate seamless data integration and impactful analytics.
Challenge
Indeed needed to grow its user base of job seekers and employers but faced challenges in reaching new users outside its channels. The enterprise needed to navigate strict data privacy laws such as the EU GDPR and US CCPA, making it challenging to share user data with media partners such as Disney for audience targeting.
Solution
Indeed partnered with Disney and used Snowflake's technology to create a safe and clean data room. Snowflake enables organizations to efficiently manage and analyze large volumes of structured and semistructured data through a scalable and fully managed platform. Snowflake’s implementation of the tool Securiti helps organizations discover sensitive personal data using powerful automation, thereby meeting the requirements of the EU GDPR and US CCPA. The Disney and Indeed partnership allowed Indeed to share encrypted data with Disney securely. Disney used this data to identify similar audiences on Hulu, a streaming service, for Indeed's ads without compromising individual user privacy. Ad effectiveness was assessed by comparing exposed and unexposed groups using Disney’s data but without sharing any specific user details.
Results
This approach enabled Indeed to reach new audiences on Disney's platforms while strictly adhering to privacy regulations. Using the data clean room led to a significant increase in revenue for Indeed, proving the effectiveness of privacy-conscious advertising strategies.
Broader Implications
The results of this case quantifiably prove that privacy and revenue growth can coexist, expediting enterprise adoption of emerging privacy-enhancing technologies. This validates data clean rooms for brands to solve engagement, targeting, and analytics use cases. This showcases how strategic priorities must evolve for marketing leaders as identifiers decline. The advent of privacy-safe data collaboration unlocks several opportunities and capabilities that were not previously feasible or were significantly limited due to privacy concerns and regulatory restrictions. As consumer expectations escalate around data transparency and control, cases such as Indeed and Disney pave the way for responsible yet outcome-driven activation. By prioritizing privacy and engaging in responsible data practices, both companies built trust with their customers. This trust translates into stronger brand loyalty and a positive reputation in the market.
Conclusion
Third-party data faces accelerated removal, but SMPC presents fresh data partnership opportunities for brands, retailers, publishers, and platforms through encrypted collaborations. The use cases and implementations discussed show substantial value creation thriving alongside full privacy protection. Industry leaders must move swiftly to evaluate secure computation solutions from providers such as Habu, LiveRamp, Optable, InfoSum, and more, launching initial platforms to unlock returns and strengthen trust.
Brands that wait too long lose out to quicker competitors already exploring secure computation's potential. Useful starting places involve measuring advertising impact, reaching more customers, and detecting fraud—simple yet valuable projects. These lay the foundations for larger future data collaborations down the road. But any delay means leaving money on the table and falling behind as regulations tighten.
Customers expect more data privacy protection. Laws also limit personal data use. So, brands must urgently understand secure computation. The technology is accessible, and the potential gains are too big to ignore. Partnering securely in terms of data enables enterprises to extract insights without exposure. Privacy and profitability can coexist. The future of responsible yet results-driven data partnerships built on cryptographic trust has arrived.
Endnotes
1 Chavez, A.; “Privacy Sandbox for the Web Reaches General Availability,” The Privacy Sandbox, 7 September 2023, https://privacysandbox.com/news/privacy-sandbox-for-the-web-reaches-general-availability/
2 European Commission, “Data Protection in the EU,” https://commission.europa.eu/law/law-topic/data-protection/data-protection-eu_en
3 Brown, ; “IAPP, “CCPA and CPRA Topic Page,” https://iapp.org/resources/topics/ccpa-and-cpra/
4 Gangrade, P.; “Suggesting New Techniques and Methods for Big Data Analysis: Privacy-Preserving Data Analysis Techniques,” Big Data Analytics Techniques for Market Intelligence, 2024, p. 265-291, USA, https://doi.org/10.4018/979-8-3693-0413-6.ch010
5 Mobey Forum, Help Me Understand Secure Multi-party Computation, February 2022, https://mobeyforum.org/download/?file=Report-1_SMPC.pdf
6 Halo refers to the influence or benefit that one product or brand has on another within the same organization's portfolio. On the other hand, cannibalization refers to when a new product or brand negatively impacts sales of another existing product or brand within the same organization's portfolio.
7 Ostwal, T.; “How Indeed Is Growing Audience and Revenue With Disney's Data Clean Room,” Adweek, 7 August 2023, https://www.adweek.com/programmatic/how-indeed-is-growing-audience-and-revenue-with-disneys-data-clean-room/
Puneet Gangrade
Is a technology and privacy analytics leader driving business transformation and data privacy solutions for global companies. He has consistently delivered multimillion-dollar business impact through advanced analytics, measurement, machine learning, and cloud platforms. His blend of adtech, martech, and analytical skills enables him to connect the dots between data, insights, and business outcomes. He is experienced with customer relationship management (CRM), data strategy, and marketing intelligence across multiple industry verticals as a client growth lead, solutions engineer, and adtech expert. Gangrade has an extensive understanding of privacy-safe data collaboration solutions, also known as data clean rooms. He was awarded Technology Marketing Engineer of the Year at the 2023 Globee Business Awards. Gangrade is also a contributing author of data analytics and marketing strategy books and has been a peer reviewer for multiple books. He can be reached at puneetgangrade@gmail.com.
