Privacy-Preserving Clinical Decision Support System Using Gaussian Kernel-Based Classification

Rahulamathavan, Yogachandran; Veluru, Suresh; Phan, Raphaël C.‐W.; Chambers, Jonathon A.; Rajarajan, Muttukrishnan

doi:10.1109/jbhi.2013.2274899

Cited by 73 publications

(62 citation statements)

References 33 publications

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“…Some articles [3,28,29] suggest methods to solve this use case. They all quantize values in order to compute on integers and justify the quantization by experimentations.…”

Section: A2 Related Work On This Use Casementioning

confidence: 99%

“…Then we testet it in the same context as experimentations from [28]. In the experimentations from [28], they use 205 support vectors for the WBC data set and 535 support vectors for the PID data set. In addition, the number of features for each sample in WBC and PID are 9 and 8 respectively and these features are between −10 −4 and 10 4 .…”

Section: A4 Performancementioning

confidence: 99%

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Boosting GSHADE Capabilities

Bringer¹,

Omri²,

Morel³

et al. 2016

Proceedings of the 21st ACM on Symposium on Access Control Models and Technologies

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The secure two-party computation (S2PC) protocols SHADE and GSHADE have been introduced by Bringer et al. in the last two years. The protocol GSHADE permits to compute different distances (Hamming, Euclidean, Mahalanobis) quite efficiently and is one of the most efficient compared to other S2PC methods. Thus this protocol can be used to efficiently compute one-to-many identification for several biometrics data (iris, face, fingerprint).In this paper, we introduce two extensions of GSHADE. The first one enables us to evaluate new multiplicative functions. This way, we show how to apply GSHADE to a classical machine learning algorithm. The second one is a new proposal to secure GSHADE against malicious adversaries following the recent dual execution and cut-andchoose strategies. The additional cost is very small. By preserving the GSHADE's structure, our extensions are very efficient compared to other S2PC methods.

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“…Some articles [3,28,29] suggest methods to solve this use case. They all quantize values in order to compute on integers and justify the quantization by experimentations.…”

Section: A2 Related Work On This Use Casementioning

confidence: 99%

Section: A4 Performancementioning

confidence: 99%

Boosting GSHADE Capabilities

Bringer¹,

Omri²,

Morel³

et al. 2016

Proceedings of the 21st ACM on Symposium on Access Control Models and Technologies

View full text Add to dashboard Cite

show abstract

“…Few of them in the literature have been redesigned for PP classification ( [8]- [15], [29] and references therein). Majority of these are developed for distributed setting where different parties hold parts of the training database and securely train a common classifier without each party needing to disclose its own training data to other parties [9], [10].…”

Section: Related Workmentioning

confidence: 99%

“…PP data classification algorithms suitable for clientserver model were studied in [8], [11]- [15], [29]. The client-server model substantially reduces the computational and communication overhead to the client since s/he needs to interact with only one server compared to the distributed setting.…”

Section: Related Workmentioning

confidence: 99%

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Efficient Privacy-Preserving Facial Expression Classification

Rahulamathavan

Rajarajan

2015

IEEE Trans. Dependable and Secure Comput.

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract-This paper proposes an efficient algorithm to perform privacy-preserving (PP) facial expression classification (FEC) in the client-server model. The server holds a database and offers the classification service to the clients. The client uses the service to classify the facial expression (FaE) of subject. It should be noted that the client and server are mutually untrusted parties and they want to perform the classification without revealing their inputs to each other. In contrast to the existing works, which rely on computationally expensive cryptographic operations, this paper proposes a lightweight algorithm based on the randomization technique. The proposed algorithm is validated using the widely used JAFFE and MUG FaE databases. Experimental results demonstrate that the proposed algorithm does not degrade the performance compared to existing works. However, it preserves the privacy of inputs while improving the computational complexity by 120 times and communication complexity by 31 percent against the existing homomorphic cryptography based approach. Permanent repository link

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EPCS: an efficient and privacy‐preserving classification service query framework for SVM

Zhu

Liu

et al. 2016

Security Comm Networks

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Support vector machine (SVM) classification, which can handle large data sets in high dimensional spaces, has been widely applied in numerous settings nowadays, such as genetic match, spam detection, and financial prediction. However, because of the data's sensitivity and classifiers' confidentiality, how to provide a privacy-preserving SVM classification has attracted considerable interest recently. Aiming at these privacy challenges, in this paper, we present an efficient and privacy-preserving classification service query framework, named EPCS, for the linear kernel SVM classifier. Specifically, based on lightweight multiparty random masking and polynomial aggregation techniques, EPCS preserves the privacy of users' data and SVM classifier efficiently during the process of user query. Through detailed security analysis, we show that EPCS can resist various security threats. In addition, we implement EPCS in Java and evaluate EPCS over a smart phone and a PC, and extensive simulation results demonstrate that the EPCS framework can indeed achieve high efficiency and effectiveness in SVM classification.

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Privacy-Preserving Clinical Decision Support System Using Gaussian Kernel-Based Classification

Cited by 73 publications

References 33 publications

Boosting GSHADE Capabilities

Boosting GSHADE Capabilities

Efficient Privacy-Preserving Facial Expression Classification

EPCS: an efficient and privacy‐preserving classification service query framework for SVM

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