This repository comprises an implementation of CRISP [1]: a novel solution that achieves utility, privacy, and integrity in the three-party model comprising a data source, a user, and a service provider.

In a nutshell, CRISP relies on quantum-resistant lattice-based approximate homomorphic encryption (HE) primitives [2] that support flexible polynomial computations on encrypted data without degrading utility. To ensure data integrity, we employ lattice-based commitments [3] and zero-knowledge proofs based on the multi-party-computation-in-the-head (or MPC-in-the-head) paradigm [4], which enable users to simultaneously convince service providers about the correctness of the encrypted data, as well as the authenticity of the underlying plaintext data, using the deployed certification mechanism.

A complete unified implementation of CRISP is implemented in Golang and available in the CRISP_go directory.

References:

[1] Chatel et al., "Privacy and Integrity Preserving Computations with CRISP", USENIX Security, 2021.

[2] Cheon et al., "Homomorphic encryption for arithmetic of approximate numbers", ASIACRYPT, 2017.

[3] Baum et al., "More Efficient Commitments from Structured Lattice Assumptions", SCN, 2018.

[4] Chase et al., "Post-Quantum Zero-Knowledge and Signatures from Symmetric-Key Primitives", CCS, 2017.

Unified implementation of CRISP in Golang.

CRISP's cicuit evaluated in the paper is implemented in two parts: ZKCE_bin and ZKCE_arith.

This directory contains the implementation of the conversion block and hash block of our soluton and evaluated in the paper. This part was implemented in C building on an implementation of ZKBoo provided by https://github.com/Sobuno/ZKBoo. Please look at the readme in the corresponding directory for further details.

This directory contains the implementation of the arithmetic part of the ZKCE evaluated in the paper: the encryption and commitment blocks. This part is implemented in C++ on top of a SNUCrypto library tailored for lattices operations over polynomial rings. Please look at the readme in the corresponding directory for further details.

This directory contains the code to evaluate the computations required for the three use-cases presented in the paper. It uses a standalone version of the Lattigo library. Please look at the readme in the corresponding directory for further details.

For our experiments we used the following datasets:

• Smart metering: UKPN dataset https://data.london.gov.uk/dataset/smartmeter-energy-use-data-in-london-households
• Disease Susceptibility: 1000 Genomes https://www.internationalgenome.org/data
• Location Based Activity Tracking: Garmin Connect https://connect.garmin.com/

The pre-processing steps of the Garmin connect dataset is presented in the usefulscripts/ directory. Please look at the readme in the corresponding directory for further details.

In this directory we provide the results of our experiments on the different datasets. The .csv files are imported to the notebook dataAnalysis.ipnb for convenience.