A Programming Language for Data Privacy with Accuracy Estimations
Licentiatavhandling, 2020

Differential privacy offers a formal framework for reasoning about the privacy and accuracy of computations on private data. It also offers a rich set of building blocks for constructing private data analyses. When carefully calibrated, these analyses simultaneously guarantee the privacy of the individuals contributing their data, and the accuracy of the data analyses results, inferring useful properties about the population. The compositional nature of differential privacy has motivated the design and implementation of several programming languages aimed at helping a data analyst in programming differentially private analyses. However, most of the programming languages for differential privacy proposed so far provide support for reasoning about privacy but not for reasoning about the accuracy of data analyses. To overcome this limitation, in this work we present DPella, a programming framework providing data analysts with support for reasoning about privacy, accuracy, and trade-offs. The distinguishing feature of DPella is a novel component that statically tracks the accuracy of different data analyses. In order to make tighter accuracy estimations, this component leverages taint analysis for automatically inferring statistical independence of the different noise quantities added for guaranteeing privacy. We evaluate our approach by implementing several classical queries from the literature and showing how data analysts can figure out the best manner to calibrate privacy to meet the accuracy requirements.

Differential Privacy

Functional Programming

Databases

Haskell

Accuracy

Concentration Bounds

Online
Opponent: Michael Hay, Colgate University, United States of America

Författare

Elisabet Lobo Vesga

Chalmers, Data- och informationsteknik, Informationssäkerhet

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Styrkeområden

Informations- och kommunikationsteknik

Ämneskategorier

Data- och informationsvetenskap

Sannolikhetsteori och statistik

Datavetenskap (datalogi)

Datorsystem

Utgivare

Chalmers tekniska högskola

Online

Online

Opponent: Michael Hay, Colgate University, United States of America

Mer information

Senast uppdaterat

2020-06-12