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Related Experiment Video

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Cortical Actin Flow in T Cells Quantified by Spatio-temporal Image Correlation Spectroscopy of Structured Illumination Microscopy Data

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Quantifying Differential Privacy in Continuous Data Release Under Temporal Correlations.

Yang Cao¹, Masatoshi Yoshikawa², Yonghui Xiao³

¹Department of Math and Computer Science, Emory University, Atlanta, GA 30322.

IEEE Transactions on Knowledge and Data Engineering

|August 23, 2019

Summary

This summary is machine-generated.

Differential Privacy (DP) mechanisms can suffer from temporal privacy leakage (TPL) when data is correlated over time. This study analyzes TPL, develops calculation methods, and proposes defenses against this growing privacy risk.

Keywords:

Differential privacy Markov model correlated data streaming data time series

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Area of Science:

Computer Science
Information Security
Data Privacy

Background:

Differential Privacy (DP) is a standard for privacy protection.
Traditional DP mechanisms often assume data independence, which is unrealistic for time-series data.
Temporal correlations in data can be exploited by adversaries, leading to privacy risks.

Purpose of the Study:

To investigate the privacy loss in DP mechanisms due to temporal data correlations.
To introduce and define Temporal Privacy Leakage (TPL).
To develop methods for calculating TPL and propose countermeasures.

Main Methods:

Modeling temporal correlations using Markov Chains.
Analyzing the event-level privacy loss of DP mechanisms over time.
Designing algorithms for TPL calculation.
Developing novel data releasing mechanisms against TPL.

Main Results:

DP mechanisms can exhibit increasing privacy loss over time, termed Temporal Privacy Leakage (TPL).
The supremum of TPL may exist under certain conditions.
Efficient algorithms for TPL calculation were developed.
Proposed mechanisms effectively mitigate TPL.

Conclusions:

Temporal correlations pose a significant, often overlooked, threat to DP.
The proposed methods provide a framework for understanding and mitigating TPL.
The developed mechanisms offer practical solutions for enhancing privacy in dynamic datasets.