Heal the Privacy: Functional Encryption and Privacy-Preserving Analytics

Autor:	Alexandros Bakas, Antonis Michalas
Rok vydání:	2022
Předmět:	FOS: Computer and information sciences Computer Science - Cryptography and Security Data_MISCELLANEOUS Cryptography and Security (cs.CR)
DOI:	10.48550/arxiv.2205.03083
Popis:	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. Comment: Originally published at IEEE Melecon 2022
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cfa414d7aadc12a16e08b400655fe133 Zobrazit plný text záznamu