PGuide: An Efficient and Privacy-Preserving Smartphone-Based Pre-Clinical Guidance Scheme

Wang, Guo‐Ming; Lu, Rongxing; Huang, Cheng

doi:10.1109/glocom.2014.7417026

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…Evaluation done by [2] revealed that over 200 businesses created since 2010 are developing a diverse set of innovative tools to make better use of available healthcare information. As the technological capabilities and understanding advance, innovators are expected to develop even more interesting ideas for using big data, some of which could help substantially reduce the soaring cost of healthcare [3].…”

Section: Introductionmentioning

confidence: 99%

PSLP: Privacy-preserving single-layer perceptron learning for e-Healthcare

Wang

Huang

2015

2015 10th International Conference on Information, Communications and Signal Processing (ICICS)

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In big data era, the explosive data mining techniques are used as popular tools to mine useful knowledge for the hospitals. However, considering the complexity of these techniques, the hospitals tend to outsource both data and calculations to computationally powerful cloud, which however poses a potential threat to user's privacy. In this paper, in order to address the privacy challenge, based on Paillier homomorphic cryptosystem, we propose a feasible privacy-preserving single-layer perceptron scheme, named PSLP. Specifically, in the proposed PSLP scheme, a hospital outsources the sensitive medical information to the cloud in ciphertext, and then the cloud can execute the privacypreserving neural network training to obtain the disease model. Detailed security analysis shows the proposed PSLP can really achieve privacy-preserving property. In addition, extensive performance evaluations also demonstrate it is feasible in terms of computational cost and communication overhead.

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Section: Introductionmentioning

confidence: 99%

PSLP: Privacy-preserving single-layer perceptron learning for e-Healthcare

Wang

Huang

2015

2015 10th International Conference on Information, Communications and Signal Processing (ICICS)

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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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“…• We propose an e cient pre-clinical guidance service scheme (PGuide) [20] designed to provide on-the-go medical guidance service while preserving user privacy. With PGuide, users can personally conduct privacy-preserving pre-clinical diagnosis based on their health profiles [8], and obtain recommendation from trusted sources (e.g.…”

Section: Research Contributionsmentioning

confidence: 99%

“…On the other hand, the healthcare center owns the disease model (a 1 , a 2 , ..., a k ) and thresholds (t 1 , t 2 , ..., t l ). The disease risk score S for the user, can be computed by S = P k i=1 a i • x i [20,114,115]. We can calculate a user's disease risk by comparing the disease risk score and the thresholds (t 1 , t 2 , ..., t l ).…”

Section: Overviewmentioning

confidence: 99%

Privacy-preserving disease detection techniques in e-healthcare systems

Wang¹

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With the increasing popularity of pervasive devices such as smartphones, Body Sensor Network(BSN), Internet-of-Things devices and cloud computing, mobile e-healthcare has become a research trend in recent years. Disease detection using big data analytics techniques is a popular e-healthcare research focus. However, big-data disease detection still faces many challenges on privacy of users' sensitive personal information, confidentiality of health service provider's diagnosis model, accuracy of diagnosis, e ciency of query, etc.In this thesis, we aim to improve the security and privacy performance of the e-healthcare systems, while achieving e cient disease detection.Firstly, we propose an e cient privacy-preserving pre-clinical guidance service scheme (PGuide) to provide on-the-go medical guidance service while preserving user privacy.Using the PGuide scheme, users can personally conduct privacy-preserving pre-clinical diagnosis based on their health profiles and obtain recommendation from trusted sources (e.g. hospitals and medical service providers) based on the diagnosis. In addition, the information transmitted to the hospitals and other medical service providers to calculate the disease risk use a disease prediction model in a privacy-preserving way.Secondly, we propose an e cient privacy-preserving health query scheme over outsourced cloud (HeOC). In this scheme, the user shortlists the possible disease on his own using self-collected health sensor data first. Then the user can query the disease severity iii accurately from the filtered result in the cloud. To reduce the query latency, we propose a novel sensor anomaly detection technique (SADS) for detecting high-risk disease. In the SADS technique, the health service provider outsources an encrypted health tree to the cloud. Authenticated users send encrypted physiological data to the cloud to detect high-risk disease without disclosing his/her sensitive personal health data. Then, with the oblivious pseudorandom function protocol (OPRF), the user retrieves the diagnosis result accurately.Thirdly, we propose an e cient and privacy-preserving priority classification scheme (PPC), for classifying encrypted patient data at the Wireless Body Area Network gateway (WBAN-gateway) in a remote e-healthcare system. Specifically, to reduce the system latency, we design a non-interactive privacy-preserving priority classification algorithm, which enables the WBAN-gateway to perform privacy-preserving priority classification on the received users medical packets by itself and relay these packets to the healthcare center according to their criticality, i.e. more critical cases get reported first.

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PGuide: An Efficient and Privacy-Preserving Smartphone-Based Pre-Clinical Guidance Scheme

Cited by 4 publications

References 7 publications

PSLP: Privacy-preserving single-layer perceptron learning for e-Healthcare

PSLP: Privacy-preserving single-layer perceptron learning for e-Healthcare

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

Privacy-preserving disease detection techniques in e-healthcare systems

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