Quantifying Differential Privacy under Temporal Correlations

Abstract

Differential Privacy (DP) has received increasing attention as a rigorous privacy framework. Many 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, continuous 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. First, we model the temporal correlations using Markov model and analyze the privacy leakage of a DP mechanism when adversaries have knowledge of such temporal correlations. Our analysis reveals that the privacy loss of a DP mechanism may accumulate and increase over time. We call it temporal privacy leakage. Second, to measure such privacy loss, we design an efficient algorithm for calculating it in polynomial time. Although the temporal privacy leakage may increase over time, we also show that its supremum may exist in some cases. Third, to bound the privacy loss, we propose mechanisms that convert any existing DP mechanism into one against temporal privacy leakage. Experiments with synthetic data confirm that our approach is efficient and effective.

An Edge Correlation Based Differentially Private Network Data Release Method

“…The work in [28] formalized the privacy preservation problem to an optimization problem by modeling the temporal correlations among contexts and further proposed an efficient contextaware privacy preserving algorithm. Cao et al [29] modeled the temporal correlations using Markov model and investigated the privacy leakage of a traditional DP mechanism under temporal correlations in the context of continuous data release. The work in [30] quantified the location correlation between two users through the similarity measurement of two hidden Markov models and applied differential privacy via private candidate sets to achieve the multiuser location correlation protection.…”

Partitioning-Based Mechanisms Under Personalized Differential Privacy

“…We observe that U ( n , ε ) can be considered as a general utility form in a series of existing state-of-the-art DP algorithms (e.g. [11, 13, 15, 3, 4, 20, 8, 12, 18, 17]). (i) Count query In the Laplace mechanism, the noisy result of a function f can be represented as f ( D ) + ν , where ν follows $\mathit{Lap}(\frac{{\mathrm{italic\Delta }}_f}{\epsilon })$, and Δ f is the sensitivity related to number of records n .…”

Cryptography for genetic material

“…Advanced applications can conceal the allele frequencies, but may require more sequencing reads to obtain the same amount of information. Importantly, due to correlations between genetic variants, applying these methods to multiple loci requires special considerations to estimate differential privacy (see [CYXX16,KM11] for estimating differential privacy with correlated data).…”