Privacy-Preserving AUC Calculation for Federated Learning using Fully Homomorphic Encryption

Concepts de base

Efficiently compute AUC in Horizontal FL systems with complete data privacy using FHE.

Résumé

Abstract

Data privacy challenge in ML applications.
Current focus on training phase privacy.
Proposed efficient, secure AUC computation method using FHE.

Introduction

Significance of FL in various fields.
Focus on Horizontal FL and model evaluation phase.

Method

Utilization of Fully Homomorphic Encryption (FHE) for privacy-preserving AUC computation.

Related Work

Comparison with DPAUC method.

Experiments

Performance analysis against ground truth AUC scores.

Conclusion

FHAUC provides robust, accurate, and secure AUC computation.

Stats

Our approach can efficiently calculate the AUC of a federated learning system involving 100 parties, achieving 99.93% accuracy in just 0.68 seconds, regardless of data size.

Citations

"Our proposed method not only guarantees complete privacy but also ensures computation robustness and provides security against a malicious aggregator."
"To address these challenges, we propose a novel AUC computation method that leverages FHE."

Idées clés tirées de

FHAUC

by Cem ... à arxiv.org 03-22-2024

https://arxiv.org/pdf/2403.14428.pdf

Questions plus approfondies

How can the use of Fully Homomorphic Encryption impact the scalability of Federated Learning systems?

Fully Homomorphic Encryption (FHE) can significantly impact the scalability of Federated Learning (FL) systems by enabling secure computations on encrypted data without decryption. This means that sensitive data remains private throughout the computation process, allowing multiple parties to collaborate on model training and evaluation without compromising data privacy.
One key advantage is that FHE allows for parallel processing, which can enhance the efficiency and speed of computations in FL systems. With FHE, each party can perform operations on their encrypted data locally before sharing only the encrypted results with a central aggregator or other parties. This distributed approach not only ensures privacy but also reduces communication overhead and latency.
Moreover, FHE enables complex calculations to be performed directly on encrypted data, eliminating the need for decryption at any point during computation. This capability enhances security by minimizing exposure to potential attacks or breaches while maintaining computational integrity.
In essence, FHE promotes scalability in FL systems by facilitating secure collaboration among multiple parties while preserving data privacy and confidentiality throughout the process.

What are the potential drawbacks or limitations of utilizing differential privacy for privacy-preserving computations in FL systems?

While using differential privacy (DP) offers a level of protection for individual samples in Federated Learning (FL) systems, there are several drawbacks and limitations associated with its implementation:

Noise Addition: DP relies on adding noise to query responses to protect individual information. However, this noise addition may lead to reduced accuracy in computations as more noise is introduced into the system.

Trade-off Between Privacy and Utility: There exists a trade-off between ensuring strong privacy guarantees through DP mechanisms and maintaining high utility or accuracy in model evaluations. Striking a balance between these two aspects is crucial but challenging.

Complexity: Implementing DP techniques requires expertise in both cryptography and machine learning, making it complex for non-specialists to deploy effectively.

Centralized Trust Model: Many DP solutions rely on centralized entities managing noise addition or aggregation processes, introducing trust concerns regarding single points of failure or malicious behavior.

5 .Scalability Issues: As datasets grow larger or when dealing with numerous participants in FL settings, scaling DP mechanisms becomes increasingly challenging due to computational constraints.
6 .Limited Protection Against Malicious Adversaries: While DP provides some defense against certain types of attacks like membership inference threats from semi-honest adversaries; it may not offer robust protection against sophisticated malicious actors aiming at extracting sensitive information from aggregated results.

How might advancements in encryption techniques like FHE influence future development of machine learning models?

Advancements in encryption techniques such as Fully Homomorphic Encryption (FHE) have significant implications for future developments in machine learning models:
1 .Enhanced Data Privacy: By enabling computations on encrypted data without decryption,Fully Homomorphic Encryption preserves user confidentiality even during model training phases where sensitive information is involved.This enhanced levelofprivacy encourages greater adoptionofmachinelearningmodelsinapplicationswhere dataprivacyisparamount,suchashealthcareandfinance.
2 .Secure Collaborative Learning: The useofFHEfacilitatessecurecollaborationamongmultiplepartiesinbuildingmachinelearningmodels.Withoutthe needtoexposeindividualdata,FHEnablesjointmodeltrainingacrossdistributeddatasetswhilemaintainingdataprivacyandconfidentiality.Thiscanfostergreatercooperationamongorganizationsandresearcherswithoutcompromisingdataprotection
3 .**ImprovedModelTransparency:**AsFHEmaintainsdataencryptionthroughoutthecomputationprocess,itcanenhancetransparencyandscrutinyoverhowmodelpredictionsaregenerated.Byallowingverifiablecomputationsonencrypteddata,FHEmodelsencouragetrustworthinessandaccountabilityinthemachinelearningpipeline
4 .**EfficientOutsourcingofComputation:WithFHE,machinelearningtasksorpredictionscanbeoutsourcedtothird-partyserviceprovidersforcomputationwithoutrevealingthesensitivedatainvolved.Thisenablessecureoffloadingofcomputationalburdenswhileupholdingdataprivacyrequirements
5 ．**AdvancedSecurityMeasures:FHEnotonlyprotectsagainstexternalthreatssuchasman-in-the-middleattacksbutalsoprovidesresilienceagainstinternalbreachesorinsiderthreatswithinitstrustedexecutionenvironment.FurtheradvancementsinFHETechnologycouldleadtoevenstrongersecurityprotocolsformachinelearningapplications