Visible to the public Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics

TitleBolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics
Publication TypeConference Paper
Year of Publication2017
AuthorsWu, Xi, Li, Fengan, Kumar, Arun, Chaudhuri, Kamalika, Jha, Somesh, Naughton, Jeffrey
Conference NameProceedings of the 2017 ACM International Conference on Management of Data
Conference LocationNew York, NY, USA
ISBN Number978-1-4503-4197-4
KeywordsData Sanitization, Differential privacy, Optimization, pubcrawl, resilience, Resiliency, Scalability, scalable data analytics, security, Stochastic computing, Stochastic Computing Security, stochastic gradient descent, virtualization privacy

While significant progress has been made separately on analytics systems for scalable stochastic gradient descent (SGD) and private SGD, none of the major scalable analytics frameworks have incorporated differentially private SGD. There are two inter-related issues for this disconnect between research and practice: (1) low model accuracy due to added noise to guarantee privacy, and (2) high development and runtime overhead of the private algorithms. This paper takes a first step to remedy this disconnect and proposes a private SGD algorithm to address both issues in an integrated manner. In contrast to the white-box approach adopted by previous work, we revisit and use the classical technique of output perturbation to devise a novel "bolt-on" approach to private SGD. While our approach trivially addresses (2), it makes (1) even more challenging. We address this challenge by providing a novel analysis of the L2-sensitivity of SGD, which allows, under the same privacy guarantees, better convergence of SGD when only a constant number of passes can be made over the data. We integrate our algorithm, as well as other state-of-the-art differentially private SGD, into Bismarck, a popular scalable SGD-based analytics system on top of an RDBMS. Extensive experiments show that our algorithm can be easily integrated, incurs virtually no overhead, scales well, and most importantly, yields substantially better (up to 4X) test accuracy than the state-of-the-art algorithms on many real datasets.

Citation Keywu_bolt-differential_2017