cryspuwaterloo / DiPSI

Differentially Private Set Intersection implemented with HELib. The official implementation for our EuroSP 2020 paper.

This is the official repository for our EuroSP 2020 paper on Differentially Private Set Intersection (DiPSI): Paper Link: TBD

Abstract

Private set intersection (PSI) allows two parties to compute the intersection of their data without revealing the data they possess that is outside of the intersection. However, in many cases of joint data analysis, the intersection is also sensitive. We define differentially private set intersection and we propose new protocols using (leveled) homomorphic encryption where the result is differentially private. Our circuit-based approach has an adaptability that allows us to achieve differential privacy, as well as to compute predicates over the intersection such as cardinality. Furthermore, our protocol produces differentially private output for set intersection and set intersection cardinality that is optimal in terms of communication and computation complexity. For a client set of size m and a server set of size n, where m is smaller than n, our communication complexity is O(m) while previous circuit-based protocols only achieve O(n+m) communication complexity. In addition to our asymptotic optimizations which include new analysis for using nested cuckoo hashing for PSI, we demonstrate the practicality of our protocol through an implementation that shows the feasibility of computing the differentially private intersection for large data sets containing millions of elements.

Description

The source code implements the for Differentially Private Set Intersection (DiPSI) using HELib (version from January 2019).

The most important files are the following

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src/params.h: A list of all parameters for the DiPSI

Client/Server: src/PSIClient., src/PSIServer.

Hashing: src/FixedBinningHash., src/HashingStrategy.

PSI helper functions: /src/PSI.*

Main: src/PSImain.*

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Setup for HELib:

Get Anaconda (https://www.anaconda.com/distribution/#linux) and set up a conda environment, install Armadillo (https://anaconda.org/conda-forge/armadillo) and NTL 10.3.0 (https://anaconda.org/conda-forge/ntl)

0. Clone this repository git clone https://https://github.com/NilsHendrikLukas/DiPSI/
1. In the ./CMakeLists.txt change the path of the NTL include and lib library to your paths i.e. the path given by the conda environment.
2. Run the cmake command in the PSI_HELib folder to compile ./CMakeLists.txt
3. (Optional) CHange parameters in the src/params.h file
4. Run cd src && make command, which creates an executable in the bin folder.
5. Run the benchmarks bin/do_benchmarks.sh (may take long time)