Abstract:Computing similarity between high-dimensional data is a fundamental problem in data mining and information retrieval, with numerous applications-such as e-discovery and patient similarity. To address the relevant performance and scalability challenges, approximation methods are employed. A common characteristic among all privacy-preserving approximation protocols based on sketching is that the sketching is performed locally and is based on common randomness. Inspired by the power of attacks on machine learning… Show more
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