Practical Differential Privacy for SQL Queries Using Elastic Sensitivity

36 citations recorded by the source corpus.

How researchers see this paper

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Ekiden: A Platform for Confidentiality-Preserving, Trustworthy, and Performant Smart Contracts (2018, neutral): “, restricting requests based on requester identity and/or a differential-privacy budget [33, 51], require persistent counters.”
CALM: Consistent Adaptive Local Marginal for Marginal Release under Local Differential Privacy (2018, methodology): “In recent years, differential privacy [13, 14] has been increasingly accepted as the de facto standard for data privacy in the research community [3, 15, 21, 24, 27].”
Cryptϵ: Crypto-Assisted Differential Privacy on Untrusted Servers (2019, background): “Support for multi-table queries like joins would require protocols for computing sensitivity [62] and data truncation [68].”
PrivateSQL: A Differentially Private SQL Query Engine (2019, background): “The privacy model in FLEX [24] considers neighboring tables that differ in exactly one row in one relation.”
Locally Differentially Private Frequent Itemset Mining (2018, methodology): “Recently, Uber has deployed a system enforcing DP during SQL queries [23], Google also proposed several works that combine DP with machine learning, e.”
Locally Differentially Private Heavy Hitter Identification (2017, methodology): “In recent years, differential privacy [11], [12] has been increasingly accepted as the de facto standard for data privacy in the research community [3], [13], [20], [22], [27].”