TL;DR
This paper addresses the challenge of sharing a privacy budget among multiple analysts in differential privacy, proposing new algorithms that satisfy key desiderata and reduce error in multi-analyst data releases.
Contribution
It introduces a formal framework for multi-analyst differential privacy, defining key desiderata, and develops novel algorithms that meet these criteria while optimizing accuracy.
Findings
Existing mechanisms fail to satisfy all desiderata.
Proposed algorithms satisfy Sharing Incentive, Non-Interference, and Adaptivity.
Empirical results show low error on realistic tasks.
Abstract
Large organizations that collect data about populations (like the US Census Bureau) release summary statistics that are used by multiple stakeholders for resource allocation and policy making problems. These organizations are also legally required to protect the privacy of individuals from whom they collect data. Differential Privacy (DP) provides a solution to release useful summary data while preserving privacy. Most DP mechanisms are designed to answer a single set of queries. In reality, there are often multiple stakeholders that use a given data release and have overlapping but not-identical queries. This introduces a novel joint optimization problem in DP where the privacy budget must be shared among different analysts. We initiate study into the problem of DP query answering across multiple analysts. To capture the competing goals and priorities of multiple analysts, we…
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