Differential privacy has emerged as the de facto standard for privacy definitions, and been used by, e.g., Apple, Google, Uber, and Microsoft, to collect sensitive information about users and to build privacy-preserving analytics engines. However, most of such advanced privacy-protection techniques are not accessible to mid-size companies and app developers in the cloud. We demonstrate a lightweight middleware DPSAaS, which provides differentially private data-sharing-and-analytics functionality as cloud services.
We focus on multi-dimensional analytical (MDA) queries under local differential privacy (LDP) in this demo. MDA queries against a fact table have predicates on (categorical or ordinal) dimensions and aggregate one or more measures. In the absence of a trusted agent, sensitive dimensions and measures are encoded in a privacy-preserving way locally using our LDP data sharing service, before being sent to the data collector. The data collector estimates the answers to MDA queries from the encoded data, using our data analytics service. We will highlight the design decisions of DPSAaS and twists made to LDA algorithms to fit the design, in order to smoothly connect DPSAaS to the data processing platform and analytics engines, and to facilitate efficient large-scale processing.
Min Xu, Tianhao Wang, Bolin Ding, Jingren Zhou, Cheng Hong, Zhicong Huang: DPSAaS: Multi-Dimensional Data Sharing and Analytics as Services under Local Differential Privacy. Proc. VLDB Endow. 12(12): 1862-1865 (2019) download