Outsourced Private Function Evaluation with Privacy Policy Enforcement

We propose a novel framework for outsourced private function evaluation with privacy policy enforcement (OPFE-PPE). Suppose an evaluator evaluates a function with private data contributed by a data contributor, and a client obtains the result of the evaluation. OPFE-PPE enables a data contributor to...

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Bibliographic Details
Main Authors: Kunihiro, Noboru, Lu, Wen-Jie, Nishide, Takashi, Sakuma, Jun
Format: Conference Proceeding
Language:English
Subjects:
Attribute-based Encryption
Cloud computing
Data privacy
Drugs
Encryption
Genetics
Homomorphic Encryption
Hospitals
Policy Oblivious Encryption
Privacy
Privacy Policy Enforcement
Private Function Evaluation
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Summary:We propose a novel framework for outsourced private function evaluation with privacy policy enforcement (OPFE-PPE). Suppose an evaluator evaluates a function with private data contributed by a data contributor, and a client obtains the result of the evaluation. OPFE-PPE enables a data contributor to enforce two different kinds of privacy policies to the process of function evaluation: evaluator policy and client policy. An evaluator policy restricts entities that can conduct function evaluation with the data. A client policy restricts entities that can obtain the result of function evaluation. We demonstrate our construction with three applications: personalized medication, genetic epidemiology, and prediction by machine learning. Experimental results show that the overhead caused by enforcing the two privacy policies is less than 10% compared to function evaluation by homomorphic encryption without any privacy policy enforcement.
ISSN:2324-9013
DOI:10.1109/TrustCom/BigDataSE.2018.00068