ConTPL

Abstract

In many real-world systems, such as Internet of Thing, sensitive data streams are collected and analyzed continually. To protect privacy, a number of mechanisms are designed to achieve ϵ-differential privacy for processing sensitive streaming data, whose privacy loss is rigorously controlled within a given parameter ϵ. However, most of the existing studies do not consider the effect of temporal correlations among the continuously generated data on the privacy loss. Our recent work reveals that, the privacy loss of a traditional DP mechanism (e.g., Laplace mechanism) may not be bounded by ϵ due to temporal correlations. We call such unexpected privacy loss Temporal Privacy Leakage (TPL). In this demonstration, we design a system, ConTPL, which is able to automatically convert an existing differentially private streaming data release mechanism into one bounding TPL within a specified level. ConTPL also provides an interactive interface and real-time visualization to help data curator understand and explore the effect of different parameters on TPL.

Efficient Q-Value Zero-Leakage Protection Scheme in SRS Regularly Publishing Private Data

“…The definition of continuous data publishing shows that privacy protection models [11,16] proposed in this scenario have a common problem. They need to anonymize all collected data.…”

“…Therefore, MS is very clumsy to handle a series of dynamic datasets. Once new data is published, continuous data publishing method [11] needs to anonymize all data. Thus, the data update is too costly.…”