Differential Privacy for Growing Databases

Part of Advances in Neural Information Processing Systems 31 (NeurIPS 2018)

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Rachel Cummings, Sara Krehbiel, Kevin A. Lai, Uthaipon Tantipongpipat


The large majority of differentially private algorithms focus on the static setting, where queries are made on an unchanging database. This is unsuitable for the myriad applications involving databases that grow over time. To address this gap in the literature, we consider the dynamic setting, in which new data arrive over time. Previous results in this setting have been limited to answering a single non-adaptive query repeatedly as the database grows. In contrast, we provide tools for richer and more adaptive analysis of growing databases. Our first contribution is a novel modification of the private multiplicative weights algorithm, which provides accurate analysis of exponentially many adaptive linear queries (an expressive query class including all counting queries) for a static database. Our modification maintains the accuracy guarantee of the static setting even as the database grows without bound. Our second contribution is a set of general results which show that many other private and accurate algorithms can be immediately extended to the dynamic setting by rerunning them at appropriate points of data growth with minimal loss of accuracy, even when data growth is unbounded.