Heal the Privacy: Functional Encryption and Privacy-Preserving Analytics

TL;DR

This paper introduces a hybrid protocol combining Functional Encryption and Differential Privacy to enable secure, privacy-preserving analytics on cloud-stored data, addressing privacy concerns in untrusted cloud environments.

Contribution

It proposes a novel hybrid cryptographic protocol that enhances privacy in cloud analytics by integrating Functional Encryption with Differential Privacy techniques.

Findings

The protocol effectively prevents privacy breaches during statistical computations.

It demonstrates improved privacy guarantees compared to traditional cryptographic methods.

The approach is practical for real-world cloud data analytics scenarios.

Abstract

Secure cloud storage is an issue of paramount importance that both businesses and end-users should take into consideration before moving their data to, potentially, untrusted clouds. Migrating data to the cloud raises multiple privacy issues, as they are completely controlled by a cloud provider. Hence, an untrusted cloud provider can potentially breach users; privacy and gain access to sensitive information. The problem becomes even more pronounced when the could provider is required to store a statistical database and periodically publish analytics. In this work, we first present a detailed example showing that the use of cryptography is not enough to ensure the privacy of individuals. Then, we design a hybrid protocol based on Functional Encryption and Differential Privacy that allows the computations of statistics in a privacy-preserving way.