On Differential Privacy for Adaptively Solving Search Problems via Sketching
Shiyuan Feng, Ying Feng, George Z. Li, Zhao Song, David P. Woodruff, Lichen Zhang

TL;DR
This paper explores how differential privacy can be applied to adaptive search problems like nearest neighbor and regression, providing algorithms that balance privacy, accuracy, and resource usage based on problem parameters.
Contribution
It extends differential privacy techniques from numerical estimation to complex search problems, offering new algorithms with provable tradeoffs.
Findings
Algorithms for private nearest neighbor search
Algorithms for private regression with turnstile updates
Tradeoffs between privacy, accuracy, and resources
Abstract
Recently differential privacy has been used for a number of streaming, data structure, and dynamic graph problems as a means of hiding the internal randomness of the data structure, so that multiple possibly adaptive queries can be made without sacrificing the correctness of the responses. Although these works use differential privacy to show that for some problems it is possible to tolerate queries using copies of a data structure, such results only apply to numerical estimation problems, and only return the cost of an optimization problem rather than the solution itself. In this paper, we investigate the use of differential privacy for adaptive queries to search problems, which are significantly more challenging since the responses to queries can reveal much more about the internal randomness than a single numerical query. We focus on two classical search…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Stochastic Gradient Optimization Techniques · Cryptography and Data Security
