Achieve Efficient and Privacy-Preserving Medical Primary Diagnosis Based on kNN

Zhu, Dan; Zhu, Hui; Liu, Ximeng; Li, Hui; Wang, Fengwei; Li, Hao

doi:10.1109/icccn.2018.8487422

Cited by 12 publications

(5 citation statements)

References 28 publications

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“…In addition, many online medical prediagnosis schemes based on various machine learning classification algorithms [26][27][28][29][30][31][32][33] adopt different privacy protection strategies. Wu et al [27] designed a new efficient and privacy-preserving conditional unintentional transmission protocol.…”

Section: Related Workmentioning

confidence: 99%

“…At the same time, in this three-party interaction scenario, the users' private information is directly transmitted without communication security. Zhu et al [33] proposed an efficient and privacy-preserving medical primary diagnosis based on kNN (called EPDK). With lightweight multiparty random shielding and polynomial aggregation technology, users can ensure the security of their sensitive information in the online medical diagnosis.…”

Section: Related Workmentioning

confidence: 99%

“…We choose two real data sets: Wisconsin Breast Cancer (WBC) and Mammographic Mass (MM) in the UCI machine learning library to evaluate the accuracy of the OMPD. In the comparative experiment, OMPD uses the Relief-kMW classifier, while OMPD-kMW uses the kMW classifier, EPDK [33] is a two-party interaction pre-diagnosis program using kNN classifiers, and PPDP [32] is a three-party interaction prediagnostic program using a single-layer perceptron trained with encryption matrices as the classifier.…”

Section: Performance Evaluationmentioning

confidence: 99%

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An Efficient Online Multiparty Interactive Medical Prediagnosis Scheme with Privacy Protection

Zhang

Zheng

Wang

2021

Wireless Communications and Mobile Computing

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Medical prediagnosis systems are now available online to give users quick and preliminary diagnosis information. The need for such a system has become particularly evident in areas with insufficient health professionals. Due to the privacy of patient medical information and the sensitivity of cloud diagnosis models, it is necessary to protect the security of data, models, and communications. These existing diagnosis systems can hardly provide a satisfied diagnosis accuracy while ensuring comprehensive security and high efficiency. In order to solve these problems, we proposed Relief- k minimum Wasserstein distance (Relief- k MW) classification method, which combined data encryption and BLS signature to form a privacy-preserving efficient online multiparty interactive medical prediagnostic scheme (OMPD). Theoretical analysis shows our OMPD effectively provides high-precision prediagnosis services. Extensive experimental results demonstrate that OMPD not only greatly improves the diagnostic accuracy but also reduces the computational and communication overhead.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

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An Efficient Online Multiparty Interactive Medical Prediagnosis Scheme with Privacy Protection

Zhang

Zheng

Wang

2021

Wireless Communications and Mobile Computing

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“…Finally, another study in Ref. [33] proposed an efficient privacy preserving disease prediction model based on kNN algorithm.…”

Section: Related Workmentioning

confidence: 99%

PPSDT: A Novel Privacy-Preserving Single Decision Tree Algorithm for Clinical Decision-Support Systems Using IoT Devices

Alabdulkarim

Al-Rodhaan

et al. 2019

Sensors

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Medical service providers offer their patients high quality services in return for their trust and satisfaction. The Internet of Things (IoT) in healthcare provides different solutions to enhance the patient-physician experience. Clinical Decision-Support Systems are used to improve the quality of health services by increasing the diagnosis pace and accuracy. Based on data mining techniques and historical medical records, a classification model is built to classify patients’ symptoms. In this paper, we propose a privacy-preserving clinical decision-support system based on our novel privacy-preserving single decision tree algorithm for diagnosing new symptoms without exposing patients’ data to different network attacks. A homomorphic encryption cipher is used to protect users’ data. In addition, the algorithm uses nonces to avoid one party from decrypting other parties’ data since they all will be using the same key pair. Our simulation results have shown that our novel algorithm have outperformed the Naïve Bayes algorithm by 46.46%; in addition to the effects of the key value and size on the run time. Furthermore, our model is validated by proves, which meet the privacy requirements of the hospitals’ datasets, frequency of attribute values, and diagnosed symptoms.

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“…As shown in [2], the protocol is much faster than publickey based protocols using homomorphic encryption. Since then, this protocol has been and is still used in many privacy-preserving solutions, e.g., [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], including support vector machines [17], facial expression classification [9], medical pre-diagnosis [18], and speaker verification [10], [11].…”

Section: Introductionmentioning

confidence: 99%

A Comment on Privacy-Preserving Scalar Product Protocols as Proposed in “SPOC”

Schneider

Treiber

2020

IEEE Trans. Parallel Distrib. Syst.

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Privacy-preserving scalar product (PPSP) protocols are an important building block for secure computation tasks in various applications. Lu et al. (TPDS'13) introduced a PPSP protocol that does not rely on cryptographic assumptions and that is used in a wide range of publications to date. In this comment paper, we show that Lu et al.'s protocol is insecure and should not be used. We describe specific attacks against it and, using impossibility results of Impagliazzo and Rudich (STOC'89), show that it is inherently insecure and cannot be fixed without relying on at least some cryptographic assumptions.

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Achieve Efficient and Privacy-Preserving Medical Primary Diagnosis Based on kNN

Cited by 12 publications

References 28 publications

An Efficient Online Multiparty Interactive Medical Prediagnosis Scheme with Privacy Protection

An Efficient Online Multiparty Interactive Medical Prediagnosis Scheme with Privacy Protection

PPSDT: A Novel Privacy-Preserving Single Decision Tree Algorithm for Clinical Decision-Support Systems Using IoT Devices

A Comment on Privacy-Preserving Scalar Product Protocols as Proposed in “SPOC”

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