Description
Linear sketches have been widely adopted to process fast data streams, and they can be used to accurately answer frequency estimation, approximate top K items, and summarize data distributions. When data are sensitive, it is desirable to provide privacy guarantees for linear sketches to preserve private information while delivering useful results with theoretical bounds. To address these challenges, we propose differentially private linear sketches with high privacy-utility trade-offs for frequency, quantile, and top K approximations.
Prochains exposés
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A non-comparison oblivious sort and its application to private k-NN
Orateur : Sofiane Azogagh - UQÀM
Sorting is a fundamental subroutine of many algorithms and as such has been studied for decades. A well-known result is the Lower Bound Theorem, which states that no comparison-based sorting algorithm can do better than O(nlog(n)) in the worst case. However, in the fifties, new sorting algorithms that do not rely on comparisons were introduced such as counting sort, which can run in linear time[…]-
Cryptography
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SoSysec
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Privacy
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Databases
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Secure storage
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