This repository includes the implementation of the paper PriCell (accepted at Patterns). Please use the following BibTex entry for citing PriCell:

@Article{Sav2022,
	author={Sav, Sinem and Bossuat, Jean-Philippe and Troncoso-Pastoriza, Juan R. and Claassen, Manfred and Hubaux, Jean-Pierre},
	title={Privacy-preserving federated neural network learning for disease-associated cell classification},
	journal={Patterns},
	year={2022},
	month={May},
	day={13},
	publisher={Elsevier},
	volume={3},
	number={5},
	issn={2666-3899},
	doi={10.1016/j.patter.2022.100487},
	url={https://doi.org/10.1016/j.patter.2022.100487}
}

PriCell (Privacy-Preserving CellCNN)

PriCell (private version) is a Golang library that ensures CellCnn[1] analysis in privacy-preserving, distributed, N-party setting.

[1] E. Arvaniti and M. Claassen. Sensitive detection of rare disease-associated cell subsets via representation learning.Nat Commun, 8:1–10, 2017

Dependencies

• cellCNN makes use of golang (go1.13.8, the code was only tested with this golang version). For more information on how to install go follow this link.
• All necessary libraries are automatically downloaded during the first execution (go modules).
• cellCNN does an intensive use of Overlay-network (ONet) library and of Lattigo.
• For more information regarding the underlying architecture please refer to the stable version of ONet go.dedis.ch/onet/v3.
• cellCNN requires Python3.7 and for data preprocessing and generation of the multi-cell inputs in cellCNN/cellCNN_clear.
• cellCNN makes use of conda 4.10.3 (the rest of the dependencies are defined under enviroment.yml).

Overview

• cellCNN/cellCNN_clear: contains the relevant data preprocessing and distribution scripts and centralized and distributed cellCnn implementation for testing/benchmarking.
  • Data generation and preprocessing scripts require Python 3.6
• cellCNN/cellCNN_optimized: contains the optimized, distributed, privacy-preserving implementation of cellCnn with multiparty homomorphic encryption
• cellCNN/cellcnnPoseidon: contains the distributed, privacy-preserving implementation of cellCnn with multiparty homomorphic encryption, relying on POSEIDON[2] packing strategies, for benchmarking
• cellCNN/eval_results: contains the results for several experimental settings and the script for plotting accuracy, precision, recall, and f-score boxplots.
• cellCNN/exampleData: contains example data generated from CMV infection (NK dataset) for running an example of distributed and privacy-preserving implementation under cellCNN/cellCNN_optimized/decentralized/example/

[2] S. Sav, A. Pyrgelis, J. R. Troncoso-Pastoriza, D. Froelicher, J.-P. Bossuat, J. S. Sousa, and J.-P. Hubaux, “POSEIDON: Privacy-Preserving Federated Neural Network Learning,” in NDSS, 2021