Online Context-aware Data Release with Sequence Information Privacy

TL;DR

This paper introduces Sequence Information Privacy (SIP), a new privacy framework for streaming data that accounts for data correlations, offering improved utility-privacy tradeoffs with lightweight mechanisms compared to existing methods.

Contribution

The paper proposes SIP, a novel privacy notion for data streams that considers correlations, along with efficient mechanisms for online data release, demonstrating significant utility improvements.

Findings

SIP provides privacy guarantees comparable to local differential privacy.

Mechanisms based on SIP outperform LDP-based methods in utility.

Experimental results show over twice the utility improvement with SIP mechanisms.

Abstract

Publishing streaming data in a privacy-preserving manner has been a key research focus for many years. This issue presents considerable challenges, particularly due to the correlations prevalent within the data stream. Existing approaches either fall short in effectively leveraging these correlations, leading to a suboptimal utility-privacy tradeoff, or they involve complex mechanism designs that increase the computation complexity with respect to the sequence length. In this paper, we introduce Sequence Information Privacy (SIP), a new privacy notion designed to guarantee privacy for an entire data stream, taking into account the intrinsic data correlations. We show that SIP provides a similar level of privacy guarantee compared to local differential privacy (LDP), and it also enjoys a lightweight modular mechanism design. We further study two online data release models (instantaneous or batched) and propose corresponding privacy-preserving data perturbation mechanisms. We provide a numerical evaluation of how correlations influence noise addition in data streams. Lastly, we conduct experiments using real-world data to compare the utility-privacy tradeoff offered by our approaches with those from existing literature. The results reveal that our mechanisms offer utility improvements more than twice those based on LDP-based mechanisms.