Pith. sign in

REVIEW 1 cited by

Heal the Privacy: Functional Encryption and Privacy-Preserving Analytics

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2205.03083 v1 pith:65JRQAZU submitted 2022-05-06 cs.CR

classification cs.CR
keywords privacycloudprovideranalyticsdataencryptionfunctionalpotentially
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original 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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Enhancing Noisy Functional Encryption for Privacy-Preserving Machine Learning

    cs.CR 2025-05 conditional novelty 6.0 of 10

    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...

Pith tools