MVG Mechanism: Differential Privacy under Matrix-Valued Query

Differential privacy mechanism design has traditionally been tailored for a scalar-valued query function. Although many mechanisms such as the Laplace and Gaussian mechanisms can be extended to a matrix-valued query function by adding i.i.d. noise to each element of the matrix, this method is often suboptimal as it forfeits an opportunity to exploit the structural characteristics typically associated with matrix analysis. To address this challenge, we propose a novel differential privacy mechanism called the Matrix-Variate Gaussian (MVG) mechanism, which adds a matrix-valued noise drawn from a matrix-variate Gaussian distribution, and we rigorously prove that the MVG mechanism preserves $(\epsilon,\delta)$-differential privacy. Furthermore, we introduce the concept of directional noise made possible by the design of the MVG mechanism. Directional noise allows the impact of the noise on the utility of the matrix-valued query function to be moderated. Finally, we experimentally demonstrate the performance of our mechanism using three matrix-valued queries on three privacy-sensitive datasets. We find that the MVG mechanism notably outperforms four previous state-of-the-art approaches, and provides comparable utility to the non-private baseline.Comment: Appeared in CCS'1

Security and Privacy for Big Data: A Systematic Literature Review

Big data is currently a hot research topic, with four million hits on Google scholar in October 2016. One reason for the popularity of big data research is the knowledge that can be extracted from analyzing these large data sets. However, data can contain sensitive information, and data must therefore be sufficiently protected as it is stored and processed. Furthermore, it might also be required to provide meaningful, proven, privacy guarantees if the data can be linked to individuals. To the best of our knowledge, there exists no systematic overview of the overlap between big data and the area of security and privacy. Consequently, this review aims to explore security and privacy research within big data, by outlining and providing structure to what research currently exists. Moreover, we investigate which papers connect security and privacy with big data, and which categories these papers cover. Ultimately, is security and privacy research for big data different from the rest of the research within the security and privacy domain? To answer these questions, we perform a systematic literature review (SLR), where we collect recent papers from top conferences, and categorize them in order to provide an overview of the security and privacy topics present within the context of big data. Within each category we also present a qualitative analysis of papers representative for that specific area. Furthermore, we explore and visualize the relationship between the categories. Thus, the objective of this review is to provide a snapshot of the current state of security and privacy research for big data, and to discover where further research is required