PrivateSQL: A Differentially Private SQL Query Engine

By Ios Kotsogiannis, Yuchao Tao, Xi He, Maryam Fanaeepour, Ashwin Machanavajjhala, Michael Hay, Gerome Miklau. 2019

138 citations recorded by the source corpus.

Differential privacy is considered a de facto standard for private data analysis. However, the definition and much of the supporting literature applies to flat tables. While there exist variants of the definition and specialized algorithms for specific types of relational data (e.g. graphs), there isn't a general privacy definition for multi-relational schemas with constraints, and no system that permits accurate differentially private answering of SQL queries while imposing a fixed privacy budget across all queries posed by the analyst. This work presents PrivateSQL, a first-of-its-kind end-to-end differentially private relational database system. PrivateSQL allows an analyst to query data stored in a standard database management system using a rich class of SQL counting queries. PrivateSQL adopts a novel generalization of differential privacy to multi-relational data that takes into account constraints in the schema like foreign keys, and allows the data owner to flexibly specify entities in the schema that need privacy. PrivateSQL ensures a fixed privacy loss across all the queries posed by the analyst by answering queries on private synopses generated from several views over the base relation that are tuned to have low error on a representative query workload. We experimentally evaluate PrivateSQL on a real-world dataset and a workload of more than 3, 600 queries. We show that for 50% of the queries PrivateSQL offers at least 1, 000x better error rates than solutions adapted from prior work.

Subfields: Artificial Intelligence, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Management Science and Operations Research

Citations in the corpus

Differentially Private SQL with Bounded User Contribution (2019, methodology): “In this work, for clarity, we keep the description of our system conceptually simple, and explicitly do not make use of techniques like smooth sensitivity [40], tight privacy budget computation methods [22, 35], variants of the differential privacy definition [6, 8, 37], adjustment of noise levels t”
LinkedIn's Audience Engagements API: A Privacy Preserving Data Analytics System at Scale (2020, methodology): “There are other open source libraries for differentially private algorithms, such as PrivateSQL [13] and the recent collaboration project between Harvard’s IQSS and Microsoft [14].”
Cryptϵ: Crypto-Assisted Differential Privacy on Untrusted Servers (2019, background): “Support for multi-table queries like joins would require protocols for computing sensitivity [62] and data truncation [68].”