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Sensitivity estimation for differentially private query processing. (arXiv:2304.09546v1 [cs.DB])
cs.CR updates on arXiv.org arxiv.org
Differential privacy has become a popular privacy-preserving method in data
analysis, query processing, and machine learning, which adds noise to the query
result to avoid leaking privacy. Sensitivity, or the maximum impact of deleting
or inserting a tuple on query results, determines the amount of noise added.
Computing the sensitivity of some simple queries such as counting query is
easy, however, computing the sensitivity of complex queries containing join
operations is challenging. Global sensitivity of such a query is unboundedly …
analysis computing data data analysis differential privacy elastic global impact large machine machine learning noise operations popular privacy private query result results simple