Free differential privacy can be achieved because of two reasons: secure computation minimizes information leakage, and the intrinsic estimation variance of the FM sketch makes the output of our protocol uncertain. The result signifies a new approach for achieving differential privacy that departs from the mainstream approach (i.e. Our further analysis revealed that if the cardinality to be estimated is large enough, our protocol can achieve (ϵ,δ) differential privacy automatically, without requiring any additional manipulation of the output. CCS '17) is computationally expensive and not scalable enough to cope with big data applications, which prompted us to design a better protocol. The state of art protocol for PDCE (Fenske et al. Our study started from building a secure computation protocol based on the Flajolet-Martin (FM) sketches, for solving the Private Distributed Cardinality Estimation (PDCE) problem, which is a fundamental problem with applications ranging from crowd tracking to network monitoring. It is natural to ask: what if we put secure computation and sketches together? We investigated the question and the findings are interesting: we can get security, we can get scalability, and somewhat unexpectedly, we can also get differential privacy-for free. On the other hand, the use of sketches has gained popularity in data mining, because sketches often give rise to highly efficient and scalable sub-linear algorithms. Secure computation is a promising privacy enhancing technology, but it is often not scalable enough for data intensive applications.
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