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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Should I trust or should I go? A deep dive into the (not so reliable) web PKI trust model
Orateur : Romain Laborde - University of Toulouse
The padlock shown in the URL bar of our favorite web browser indicates that we are connected using a secure HTTPS connection and providing some sense of security. Unfortunately, the reality is slightly more complex. The trust model of the underlying Web PKI is invalid, making TLS a colossus with feet of clay. In this talk, we will dive into the trust model of the web PKI ecosystem to understand[…]-
SoSysec
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Protocols
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Network
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