Data Sanitisation Protocols for the Privacy Funnel with Differential Privacy Guarantees
Milan Lopuha\"a-Zwakenberg, Haochen Tong, Boris \v{S}kori\'c

TL;DR
This paper introduces efficient local differential privacy protocols that improve data sanitisation for open data sharing, balancing privacy guarantees with utility, and addresses computational challenges in the Privacy Funnel approach.
Contribution
It proposes optimal local privacy protocols under differential privacy metrics, introduces Side-channel Resistant Local Information Privacy, and presents Conditional Reporting for practical scenarios.
Findings
Efficient protocols under Local Differential Privacy and Local Information Privacy.
Side-channel Resistant Local Information Privacy for multi-attribute data.
Conditional Reporting protocol performs well on real and synthetic data.
Abstract
In the Open Data approach, governments and other public organisations want to share their datasets with the public, for accountability and to support participation. Data must be opened in such a way that individual privacy is safeguarded. The Privacy Funnel is a mathematical approach that produces a sanitised database that does not leak private data beyond a chosen threshold. The downsides to this approach are that it does not give worst-case privacy guarantees, and that finding optimal sanitisation protocols can be computationally prohibitive. We tackle these problems by using differential privacy metrics, and by considering local protocols which operate on one entry at a time. We show that under both the Local Differential Privacy and Local Information Privacy leakage metrics, one can efficiently obtain optimal protocols. Furthermore, Local Information Privacy is both more closely…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Cryptography and Data Security · Privacy, Security, and Data Protection
