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2016 IEEE 32nd International Conference on Data Engineering (ICDE) (2016)
Helsinki, Finland
May 16, 2016 to May 20, 2016
ISBN: 978-1-5090-2020-1
pp: 217-228
Erman Pattuk , University of Texas at Dallas, Richardson, USA
Murat Kantarcioglu , University of Texas at Dallas, Richardson, USA
Huseyin Ulusoy , University of Texas at Dallas, Richardson, USA
Bradley Malin , Vanderbilt University, Nashville, Tennessee USA
Recent advances in personalized medicine point towards a future where clinical decision making will be dependent upon the individual characteristics of the patient, e.g., their age, race, genomic variation, and lifestyle. Already, there are numerous commercial entities working towards the provision of software to support such decisions as cloud-based services. However, deployment of such services in such settings raises important challenges for privacy. A recent attack shows that disclosing personalized drug dosage recommendations, combined with several pieces of demographic knowledge, can be leveraged to infer single nucleotide polymorphism variants of a patient. One manner to prevent such inference is to apply secure multi-party computation (SMC) techniques that hide all patient data, so that no information, including the clinical recommendation, is disclosed during the decision making process. Yet, SMC is a computationally cumbersome process and disclosing some information may be necessary for various compliance purposes. Additionally, certain information (e.g., demographic information) may already be publicly available. In this work, we provide a novel approach to selectively disclose certain information before the SMC process to significantly improve personalized decision making performance while preserving desired levels of privacy. To achieve this goal, we introduce mechanisms to quickly compute the loss in privacy due to information disclosure while considering its performance impact on SMC execution phase. Our empirical analysis show that we can achieve up to three orders of magnitude improvement compared to pure SMC solutions with only a slight increase in privacy risks.
Servers, Privacy, Protocols, Genomics, Bioinformatics, Decision trees, Medical services

E. Pattuk, M. Kantarcioglu, H. Ulusoy and B. Malin, "Optimizing secure classification performance with privacy-aware feature selection," 2016 IEEE 32nd International Conference on Data Engineering (ICDE), Helsinki, Finland, 2016, pp. 217-228.
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