Employing Differential Privacy (DP), the state-of-the-art privacy standard, to answer aggregate database queries poses new challenges for users to understand the trends and anomalies observed in the query results: Is the unexpected answer due to the data itself, or is it due to the extra noise that must be added to preserve DP? We propose to demonstrate DPXPlain, the first system for explaining group-by aggregate query answers with DP. DPXPlain allows users to compare values of two groups and receive a validity check, and further provides an explanation table with an interactive visualization, containing the approximately 'top-k' explanation predicates along with their relative influences and ranks in the form of confidence intervals, while guaranteeing DP in all steps.
This content will become publicly available on July 12, 2025
Statistical Privacy and Consent in Data Aggregation
As new laws governing management of personal data are introduced, e.g., the European Union’s General Data Protection Regulation of 2016 and the California Consumer Privacy Act of 2018, compliance with data governance legislation is becoming an increasingly important aspect of data management. An important component of many data privacy laws is that they require companies to only use an individual’s data for a purpose the individual has explicitly consented to. Prior methods for enforcing consent for aggregate queries either use access control to eliminate data without consent from query evaluation or apply differential privacy algorithms to inject synthetic noise into the outcomes of queries (or input data) to ensure that the anonymity of non-consenting individuals is preserved with high probability. Both approaches return query results that differ from the ground truth results corresponding to the full input containing data from both consenting and non-consenting individuals. We present an alternative frame-
work for group-by aggregate queries, tailored for applications, e.g., medicine, where even a small deviation from the correct answer to a query cannot be tolerated. Our approach uses provenance to determine, for each output tuple of a group-by aggregate query, which individual’s data was used to derive the result for this group. We then use statistical tests to determine how likely it is that the presence of data for a non-consenting individual will be revealed
by such an output tuple. We filter out tuples for which this test fails, i.e., which are deemed likely to reveal non-consenting data. Thus, our approach always returns a subset of the ground truth query answers. Our experiments successfully return only 100% accurate results in instances where access control or differential privacy would have either returned less total or less accurate results.
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- Award ID(s):
- 2016548
- NSF-PAR ID:
- 10528465
- Publisher / Repository:
- The 36th International Conference on Scientific and Statistical Database Management (SSDBM)
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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