DPolicy: Managing Privacy Risks Across Multiple Releases with Differential Privacy (2505.06747v1)

Abstract: Differential Privacy (DP) has emerged as a robust framework for privacy-preserving data releases and has been successfully applied in high-profile cases, such as the 2020 US Census. However, in organizational settings, the use of DP remains largely confined to isolated data releases. This approach restricts the potential of DP to serve as a framework for comprehensive privacy risk management at an organizational level. Although one might expect that the cumulative privacy risk of isolated releases could be assessed using DP's compositional property, in practice, individual DP guarantees are frequently tailored to specific releases, making it difficult to reason about their interaction or combined impact. At the same time, less tailored DP guarantees, which compose more easily, also offer only limited insight because they lead to excessively large privacy budgets that convey limited meaning. To address these limitations, we present DPolicy, a system designed to manage cumulative privacy risks across multiple data releases using DP. Unlike traditional approaches that treat each release in isolation or rely on a single (global) DP guarantee, our system employs a flexible framework that considers multiple DP guarantees simultaneously, reflecting the diverse contexts and scopes typical of real-world DP deployments. DPolicy introduces a high-level policy language to formalize privacy guarantees, making traditionally implicit assumptions on scopes and contexts explicit. By deriving the DP guarantees required to enforce complex privacy semantics from these high-level policies, DPolicy enables fine-grained privacy risk management on an organizational scale. We implement and evaluate DPolicy, demonstrating how it mitigates privacy risks that can emerge without comprehensive, organization-wide privacy risk management.

Overview of DPolicy: Managing Privacy Risks Across Multiple Releases with Differential Privacy

The paper, "DPolicy: Managing Privacy Risks Across Multiple Releases with Differential Privacy," elaborates on the challenges and strategies related to employing differential privacy (DP) in real-world, organizational settings, particularly when data is released through multiple iterations or contexts. Differential Privacy has historically been viewed more as a mechanism to protect the privacy of individuals within specific data releases, yet its application at an organizational scale remains inherently complex due to numerous factors, such as the interaction between multiple DP guarantees and their cumulative privacy risks.

Key Contributions

This research introduces the DPolicy system, which seeks to mitigate these challenges by treating DP guarantees not in isolation but as part of a broader context-aware framework. The primary contributions of the paper can be summarized as follows:

1. DP Policy Language: The paper proposes a high-level policy language that allows organizations to articulate explicit privacy semantics rather than rely on typically implicit assumptions. This language facilitates the systematic definition of privacy guarantees across various contexts and scopes, thereby promoting transparency and accountability in DP practices.
2. Policy Enforcement Mechanism: The authors have developed methods to enforce and optimize privacy policies efficiently. This involves pruning redundant rules that do not contribute to constraining the privacy budget, thereby improving scalability without compromising privacy risk management.
3. Integration with Existing Systems: DPolicy integrates seamlessly with existing DP budget management systems, exemplified by its implementation with Cohere. This integration exemplifies the possibility of addressing cumulative organizational privacy risks while maintaining efficiency in budget allocation across multiple releases.

Strong Numerical Results and Semantics

The paper evaluates DPolicy through simulated workloads, contrasting its efficacy against Cohere. The results indicate that DPolicy provides significant advantages in managing privacy constraints without sacrificing utility. Unlike traditional approaches which can expose undue privacy risks by violating contextual constraints, DPolicy leverages its policy language to enforce nuanced privacy semantics effectively. For example, when accounting for context-specific privacy guarantees, DPolicy consistently maintains privacy costs within acceptable boundaries, whereas Cohere incurs significant violations due to its static approach.

Implications and Future Directions

The implications of this research are considerable: DPolicy sets a precedent for future privacy risk management solutions by underscoring the necessity for dynamic, context-aware, and fine-grained privacy guarantees. In practical terms, adopting DPolicy could enable organizations to comply more robustly with privacy regulations, such as GDPR or CCPA, by demonstrating comprehensive privacy governance across both individual and cumulative data releases.

Theoretical implications are equally significant as the work encourages a reevaluation of DP practices, advocating for more intricate models of privacy loss that encapsulate organizational dynamics over isolated guarantees. This aligns with broader discussions in privacy research regarding defining contexts and scopes that reflect real-world data practices.

As AI and DP technologies continue to evolve, future research may further refine these strategies, exploring how AI-driven approaches can automate rule set optimizations and extend context-aware privacy guarantees even further. This can potentially unlock new possibilities in domains that require strict adherence to privacy, such as health informatics and secure multi-party computations.

In summary, DPolicy represents a strategic leap forward in bridging the gap between the conceptual potential of DP and its practical implementation across complex organizational landscapes. Its contributions are poised to guide both current and future developments in privacy risk management in AI-enhanced data systems.