A privacy-preserving solution for the bipartite ranking problem

Simple item page
dc.citation.epage380en_US
dc.citation.spage375en_US
dc.contributor.authorFaramarzi, Noushin Saleken_US
dc.contributor.authorAyday, Ermanen_US
dc.contributor.authorGüvenir, H. Altayen_US
dc.coverage.spatialAnaheim, CA, USA
dc.date.accessioned2018-04-12T11:46:04Z
dc.date.available2018-04-12T11:46:04Z
dc.date.issued2016-12en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionConference name: 15th IEEE International Conference on Machine Learning and Applications (ICMLA) 2016
dc.descriptionDate of Conference: 18-20 Dec. 2016
dc.description.abstractIn this paper, we propose an efficient solution for the privacy-preserving of a bipartite ranking algorithm. The bipartite ranking problem can be considered as finding a function that ranks positive instances (in a dataset) higher than the negative ones. However, one common concern for all the existing schemes is the privacy of individuals in the dataset. That is, one (e.g., a researcher) needs to access the records of all individuals in the dataset in order to run the algorithm. This privacy concern puts limitations on the use of sensitive personal data for such analysis. The RIMARC (Ranking Instances by Maximizing Area under the ROC Curve) algorithm solves the bipartite ranking problem by learning a model to rank instances. As part of the model, it learns weights for each feature by analyzing the area under receiver operating characteristic (ROC) curve. RIMARC algorithm is shown to be more accurate and efficient than its counterparts. Thus, we use this algorithm as a building-block and provide a privacy-preserving version of the RIMARC algorithm using homomorphic encryption and secure multi-party computation. Our proposed algorithm lets a data owner outsource the storage and processing of its encrypted dataset to a semi-trusted cloud. Then, a researcher can get the results of his/her queries (to learn the ranking function) on the dataset by interacting with the cloud. During this process, neither the researcher nor the cloud learns any information about the raw dataset. We prove the security of the proposed algorithm and show its efficiency via experiments on real data.en_US
dc.identifier.doi10.1109/ICMLA.2016.0067en_US
dc.identifier.urihttp://hdl.handle.net/11693/37625
dc.language.isoEnglishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttps://doi.org/10.1109/ICMLA.2016.0067en_US
dc.source.titleProceedings -15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016en_US
dc.subjectArtificial intelligenceen_US
dc.subjectCryptographyen_US
dc.subjectData handlingen_US
dc.subjectDigital storageen_US
dc.subjectHistoric preservationen_US
dc.subjectLearning algorithmsen_US
dc.subjectLearning systemsen_US
dc.subjectArea under the ROC curveen_US
dc.subjectHo-momorphic encryptionsen_US
dc.subjectPositive instancesen_US
dc.subjectPrivacy preservingen_US
dc.subjectPrivacy preserving solutionsen_US
dc.subjectRanking algorithmen_US
dc.subjectReceiver operating characteristic curvesen_US
dc.subjectSecure multi-party computationen_US
dc.subjectData privacyen_US
dc.titleA privacy-preserving solution for the bipartite ranking problemen_US
dc.typeConference Paperen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
A privacy-preserving solution for the bipartite ranking problem.pdf
Size:
491.38 KB
Format:
Adobe Portable Document Format
Description:
Full Printable Version
Download
Collections
Department of Computer Engineering