Calculating Function Sensitivity for Synthetic Data Algorithms
Paper i proceeding, 2023

Differential privacy (DP) provides a robust framework for ensuring individual privacy while analyzing population data. To achieve DP, statistical noise is added to query results before publication, but accurately determining the required noise is challenging, especially for user-defined functions. Existing approaches often rely on limited pre-defined functions with known sensitivities, limiting the expressivity of DP systems. In this paper, we present a novel embedded domain-specific language (eDSL) in Haskell to automatically approximate the sensitivity of user-defined linear functions commonly used in synthetic data generation. Our approach leverages Haskell's expressive type system and generic programming principles to infer function ranges, enabling us to approximate sensitivities efficiently. We demonstrate the effectiveness of our eDSL by integrating it into the Multiplicative Weights Exponential Mechanism (MWEM) for synthetic data generation. Our solution guides users when updating functions, ensuring proper sensitivity consideration, enhancing the robustness and reliability of synthetic data algorithms. By adopting this straightforward yet effective approach, we streamline the sensitivity calculation process for user-defined functions, making it more accessible and user-friendly. The contributions of our work include an eDSL capable of approximating sensitivity for linear functions and its evaluation within the context of MWEM workloads.

Partial evaluation

Haskell

Synthetic data

eDSL

Differential Privacy

Författare

Markus Pettersson

Student vid Chalmers

Johannes Ljung Ekeroth

Student vid Chalmers

Alejandro Russo

Chalmers, Data- och informationsteknik, Informationssäkerhet

DPella AB

PROCEEDINGS OF THE 2023 35TH SYMPOSIUM ON IMPLEMENTATION AND APPLICATION OF FUNCTIONAL LANGUAGES, IFL 2023

6
979-8-4007-1631-7 (ISBN)

35th Symposium on Implementation and Application of Functional Languages (IFL)
Braga, Portugal,

A Programming Framework for Differential Privacy with Accuracy Calculation

Vetenskapsrådet (VR) (2020-03881), 2020-12-01 -- 2024-11-30.

Octopi: Säker Programering för Sakernas Internet

Stiftelsen för Strategisk forskning (SSF) (RIT17-0023), 2018-03-01 -- 2023-02-28.

Ämneskategorier

Datavetenskap (datalogi)

Datorsystem

DOI

10.1145/3652561.3652567

Mer information

Senast uppdaterat

2024-10-07