On the tradeoff between privacy and utility in data publishing

By Tiancheng Li, Ninghui Li. Knowledge Discovery and Data Mining, 2009

365 citations recorded by the source corpus.

How researchers see this paper

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Minimax Filter: Learning to Preserve Privacy from Inference Attacks (2016, methodology): “Regarding the use of risks in this paper, classification error-based quantities have been suggested in the literature (Iyengar, 2002; Brickell and Shmatikov, 2008; Li and Li, 2009).”
Slicing: A New Approach to Privacy Preserving Data Publishing (2009, background): “[20-52] * 4790* dyspepsia [20-52] * 4790* flu [20-52] * 4790* flu [20-52] * 4790* bronchitis”
Measuring Large-Scale Social Networks with High Resolution (2014, neutral): “In order to avoid re-identification and consequent malicious usage of data, PII can be completely removed, hidden by aggregation, or transformed to be less identifiable, resulting in a trade-off between privacy and utility [78].”
Utility-Privacy Tradeoffs in Databases: An Information-Theoretic Approach (2011, background): “There has been some work pointing out the loss of utility due to privacy mech anisms for specific applications [14].”
Anonymization of location data does not work: a large-scale measurement study (2011, methodology): “Random sampling or random noise insertion impacts the utility of data and the tradeoff between utility and privacy need to be carefully considered [21, 17, 2].”