Differentially private algorithms for answering sets of predicate counting queries on a sensitive database have many applications. Organizations that collect individual-level data, such as statistical agencies and medical institutions, use them to safely release summary tabulations. However, existing techniques are accurate only on a narrow class of query workloads, or are extremely slow, especially when analyzing more than one or two dimensions of the data.In this work we propose HDMM, a new differentially private algorithm for answering a workload of predicate counting queries, that is especially effective for higher-dimensional datasets. HDMM represents query workloads using an implicit matrix representation and exploits this compact representation to efficiently search (a subset of) the space of differentially private algorithms for one that answers the input query workload with high accuracy. We empirically show that HDMM can efficiently answer queries with lower error than state-of-the-art techniques on a variety of low and high dimensional datasets.Organization. In addition to the sections noted above, we describe our use case next, followed by background, and the end-to-end algorithm components in Section 7, experiments in Section 8, and discussion in Section 9.

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Software piracy is an important concern for application developers. Such concerns are especially relevant in mobile application development, where piracy rates can be greater than 90%. The most common approach used by mobile developers to prevent piracy is code obfuscation. However, the decision to apply such transformations is currently made without regard to the impacts of obfuscations on another area of increasing concern for mobile application developers, energy usage. Because both software piracy and battery life are important concerns, mobile application developers must strike a balance between protecting their applications and preserving the battery lives of their users' devices. To help them make such choices, we conducted an empirical study of the effects of 18 code obfuscations on the amount of energy consumed by executing a total of 21 usage scenarios spread across 11 Android applications on four different mobile phone platforms. The results of the study indicate that, while obfuscations can have a statistically significant impact on energy usage and are more likely to increase energy usage than to decrease energy usage, the magnitudes of such impacts are unlikely to be meaningful to mobile application users. Copyright © 2016 John Wiley & Sons, Ltd.

We propose a general approach for differentially private synthetic data generation, that consists of three steps: (1) select a collection of low-dimensional marginals, (2) measure those marginals with a noise addition mechanism, and (3) generate synthetic data that preserves the measured marginals well. Central to this approach is Private-PGM, a post-processing method that is used to estimate a high-dimensional data distribution from noisy measurements of its marginals. We present two mechanisms, NIST-MST and MST, that are instances of this general approach. NIST-MST was the winning mechanism in the 2018 NIST differential privacy synthetic data competition, and MST is a new mechanism that can work in more general settings, while still performing comparably to NIST-MST. We believe our general approach should be of broad interest, and can be adopted in future mechanisms for synthetic data generation.

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