Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2018 | TrustCom/BigDataSE | Homomorphic encryption,Computer security,Computer science,Attribute-based encryption,Privacy policy,Enforcement |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Noboru Kunihiro | 1 | 425 | 45.72 |
| Wenjie Lu | 2 | 14 | 2.68 |
| Takashi Nishide | 3 | 357 | 27.86 |
| Jun Sakuma | 4 | 345 | 37.29 |