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Heal the Privacy: Functional Encryption and Privacy-Preserving Analytics
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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.
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Cited by 1 Pith paper
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Enhancing Noisy Functional Encryption for Privacy-Preserving Machine Learning
The authors define dynamic noisy multi-client functional encryption, present the PRF-based inner-product scheme DyNo, and use it to train a differentially private logistic regression with claimed millisecond-level per...
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