Conference Proceedings
Oblivious Sampling Algorithms for Private Data Analysis
Sajin Sasy, Olga Ohrimenko, H Wallach (ed.), H Larochelle (ed.), A Beygelzimer (ed.), F d'Alche-Buc (ed.), E Fox (ed.), R Garnett (ed.)
Advances in Neural Information Processing Systems 32 (NeurIPS 2019) | NeulPS | Published : 2019
Abstract
We study secure and privacy-preserving data analysis based on queries executed on samples from a dataset. Trusted execution environments (TEEs) can be used to protect the content of the data during query computation, while supporting differential-private (DP) queries in TEEs provides record privacy when query output is revealed. Support for sample-based queries is attractive due to \emph{privacy amplification} since not all dataset is used to answer a query but only a small subset. However, extracting data samples with TEEs while proving strong DP guarantees is not trivial as secrecy of sample indices has to be preserved. To this end, we design efficient secure variants of common sampling al..
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