Performance-Oriented Privacy-Preserving Data Integration Export

@incollection{citeulike:5829008, abstract = {Current solutions to integrating private data with public data have provided useful privacy metrics, such as relative information gain, that can be used to evaluate alternative approaches. Unfortunately, they have not addressed critical performance issues, especially when the public database is very large. The use of hashes and noise yields better performance than existing techniques, while still making it difficult for unauthorized entities to distinguish which data items truly exist in the private database. As we show here, the uncertainty introduced by collisions caused by hashing and the injection of noise can be leveraged to perform a privacy-preserving relational join operation between a massive public table and a relatively smaller private one.}, author = {Pon, Raymond K. and Critchlow, Terence}, citeulike-article-id = {5829008}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/11530084_19}, citeulike-linkout-1 = {http://www.springerlink.com/content/5gkyed74k6tx3med}, doi = {10.1007/11530084_19}, journal = {Data Integration in the Life Sciences}, keywords = {data\_integration, privacy\_data}, pages = {240--256}, posted-at = {2009-09-23 12:10:33}, priority = {3}, title = {Performance-Oriented Privacy-Preserving Data Integration}, url = {http://dx.doi.org/10.1007/11530084_19}, year = {2005} }

TY - CHAP ID - citeulike:5829008 L3 - citeulike-article-id:5829008 N2 - Current solutions to integrating private data with public data have provided useful privacy metrics, such as relative information gain, that can be used to evaluate alternative approaches. Unfortunately, they have not addressed critical performance issues, especially when the public database is very large. The use of hashes and noise yields better performance than existing techniques, while still making it difficult for unauthorized entities to distinguish which data items truly exist in the private database. As we show here, the uncertainty introduced by collisions caused by hashing and the injection of noise can be leveraged to perform a privacy-preserving relational join operation between a massive public table and a relatively smaller private one. JF - Data Integration in the Life Sciences EP - 256 TI - Performance-Oriented Privacy-Preserving Data Integration SP - 240 KW - data_integration KW - privacy_data AU - Pon, Raymond AU - Critchlow, Terence PY - 2005/// UR - http://dx.doi.org/10.1007/11530084_19 ER -