Abstract-This paper presents and model a novel general framework for privacy aware collaborative information sharing for data analysis. Collaborative information sharing systems can be cross-domain, involve different data providers which might also be competitors. For this reason, shared information may imply privacy concerns, which must be addressed, applying privacy preserving mechanisms on information before sharing them. However, since the application of these privacy preserving mechanisms may negatively affect the accuracy of data analysis, a trade-off must be considered, and the privacy preserving mechanism to be applied must be chosen correctly. The proposed framework is based on the separation between a first level which enforces information privacy as specified by data providers, and a second level which performs data analysis on the sanitized data. The proposed framework defines and models a workflow which applies to any privacy aware collaborative information sharing system, defines indexes to measure the compatibility between privacy requirements, and includes a novel method to compute the trade-off between privacy and accuracy. This work also proposes a methodology to choose, case-by-case, the privacy mechanism which maximizes the trade-off between privacy and accuracy. An applicative example on a real dataset with more than 30k records is also presented.
The fundamental trade-off between privacy and accuracy was studied in REF , where the authors proposed a general model for collaborative information analysis systems to determine which privacy mechanism optimizes the trade-off between privacy and accuracy.
Modeling Privacy Aware Information Sharing Systems: A Formal and General Approach
2016 IEEE Trustcom/BigDataSE/ISPA
2016 IEEE Trustcom/BigDataSE/ISPA
Computer Science