PrivateClean: Data Cleaning and Differential Privacy

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How researchers see this paper

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Collecting and Analyzing Multidimensional Data with Local Differential Privacy (2019, background): “Differential privacy [16] is a strong privacy standard that provides semantic, information-theoretic guarantees on individuals’ privacy, which has attracted much attention from various fields, including data management [8], [10], [27], machine learning [4], theory [5], [13], [25], and systems [6].”
A survey on provenance: What for? What form? What from? (2017, background): “, [60,86,117,186] consider trust, [145] considers integrity and accuracy).”
Towards reliable interactive data cleaning: a user survey and recommendations (2016, background): “…of aspects of this problem, including reducing the latency of human operations [20], understanding quality in macrotasks [18], reducing latency with sampling [29], mitigating cognitive biases [27], addressing privacy concerns [30], and defining statistical correctness for data cleaning [28] [31].”