Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Rethinking rank swapping to decrease disclosure risk
Data & Knowledge Engineering
PRAM optimization using an evolutionary algorithm
PSD'10 Proceedings of the 2010 international conference on Privacy in statistical databases
| Hi-index | 0.00 |
The continuous growing amount of public sensible data has increased the risk of breaking the privacy of people or institutions in those datasets. Many protection methods have been developed to solve this problem by either distorting or generalizing data but taking into account the difficult tradeoff between data utility (information loss) and protection against disclosure (disclosure risk). In this paper we present an optimization approach for data protection based on an evolutionary algorithm which is guided by a combination of information loss and disclosure risk measures. In this way, state-of-the-art protection methods are combined to obtain new data protections with a better trade-off between these two measures. The paper presents several experimental results that assess the performance of our approach.