DPXPlain: Privately Explaining Aggregate Query Answers
Yuchao Tao, Amir Gilad, Ashwin Machanavajjhala, Sudeepa Roy

TL;DR
DPXPlain is a novel system that provides private explanations for aggregate query answers under differential privacy, helping users understand data trends and anomalies without compromising privacy.
Contribution
It introduces the first framework for explaining group-by aggregate query answers with differential privacy, including comparison and explanation modules.
Findings
Effective explanation of aggregate answers with DP guarantees
High accuracy in identifying true data trends despite noise
Efficient performance on real and synthetic datasets
Abstract
Differential privacy (DP) is the state-of-the-art and rigorous notion of privacy for answering aggregate database queries while preserving the privacy of sensitive information in the data. In today's era of data analysis, however, it 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? In the second case, even the observation made by the users on query results may be wrong. In the first case, can we still mine interesting explanations from the sensitive data while protecting its privacy? To address these challenges, we present a three-phase framework DPXPlain, which is the first system to the best of our knowledge for explaining group-by aggregate query answers with DP. In its three phases, DPXPlain (a) answers a group-by…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Cryptography and Data Security · Data Quality and Management
