A Programming Framework for Differential Privacy with Accuracy Concentration Bounds
Paper i proceeding, 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.

Functional Programming

Databases

Concentration Bounds

Haskell

Differential Privacy

Accuracy

Författare

Elisabet Lobo Vesga

Chalmers, Data- och informationsteknik, Informationssäkerhet

Alejandro Russo

Chalmers, Data- och informationsteknik, Informationssäkerhet

Marco Gaboardi

Boston University

IEEE Security and Privacy

1540-7993 (ISSN)

1314-1331

41st IEEE Symposium on Security and Privacy
San Francisco, USA,

WebSec: Säkerhet i webb-drivna system

Stiftelsen för Strategisk forskning (SSF), 2018-03-01 -- 2023-02-28.

Octopi: Säker Programering för Sakernas Internet

Stiftelsen för Strategisk forskning (SSF), 2018-03-01 -- 2023-02-28.

Styrkeområden

Informations- och kommunikationsteknik

Ämneskategorier

Data- och informationsvetenskap

Sannolikhetsteori och statistik

Datavetenskap (datalogi)

Datorsystem

DOI

10.1109/SP40000.2020.00086

Mer information

Senast uppdaterat

2020-05-19