On the Tradeoff Between Explainability and Privacy in Databases with Amir Gilad of Duke University
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Explanations for query results have been the subject of extensive research. The advantages of such explanations are evident, as they allow users to validate and justify the results of the query and deepen their knowledge about the data. However, when the query is proprietary and needs to remain confidential or when the data is cloaked by privacy restrictions, such explanations may be detrimental to the privacy desiderata. This tradeoff raises the question “can we provide useful explanations while maintaining the privacy requirements of the query and data?”
In this talk, Amir Gilad presents two recent works that attempt to reconcile these gaps. He discusses work for providing provenance-based explanations for query results, while ensuring that a proprietary query remains hidden using a privacy model inspired by k-anonymity. He also presents our work on providing predicate-based explanations for aggregate query results, while ensuring differential privacy.