Quantifying Differential Privacy under Temporal Correlations

Understand

Differential Privacy (DP) has received increased attention as a rigorous privacy framework.

• Existing studies employ traditional DP mechanisms (e.g., the Laplace mechanism) as primitives, which assume that the data are independent, or that adversaries do not have knowledge of the data correlations.
• However, continuously generated data in the real world tend to be temporally correlated, and such correlations can be acquired by adversaries.
• In this paper, we investigate the potential privacy loss of a traditional DP mechanism under temporal correlations in the context of continuous data release.