Privacy-preserving Naive Bayes classifiers secure against the substitution-then-comparison attack

Gao, Chongzhi; Cheng, Qiong; He, Pei; Susilo, Willy; Li, Jin

doi:10.1016/j.ins.2018.02.058

Cited by 172 publications

(84 citation statements)

References 22 publications

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“…However, it may leak privacy information through statistical inference, even though the perturbed data is hard to be reconstructed [8,[16][17][18][19][20][21][22][23]. However, many limitations exist for perturbation.…”

Section: Related Workmentioning

confidence: 99%

An information-aware visualization for privacy-preserving accelerometer data sharing

Xiao

Zhao

et al. 2018

Hum. Cent. Comput. Inf. Sci.

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In the age of big data, plenty of valuable sensing data have been shared to enhance scientific innovation. However, this may cause unexpected privacy leakage. Although numerous privacy preservation techniques, such as perturbation, encryption, and anonymization, have been proposed to conceal sensitive information, it is usually at the cost of the application utility. Moreover, most of the existing works did not distinguished the underlying factors, such as data features and sampling rate, which contribute differently to utility and privacy information implied in the shared data. To well balance the application utility and privacy leakage for data sharing, we utilize mutual information and visualization techniques to analyze the impact of the underlying factors on utility and privacy, respectively, and design an interactive visualization tool to help users identify the appropriate solution to achieve the objectives of high application utility and low privacy leakage simultaneously. To illustrate the effectiveness of the proposed scheme and tool, accelerometer data collected from mobile devices have been adopted as an illustrative example. Experimental study has shown that feature selection and sampling frequency play dominant roles in reducing privacy leakage with much less reduction on utility, and the proposed visualization tool can effectively recommend the appropriate combination of features and sampling rates that can help users make decision on the trade-off between utility and privacy.

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Section: Related Workmentioning

confidence: 99%

An information-aware visualization for privacy-preserving accelerometer data sharing

Xiao

Zhao

et al. 2018

Hum. Cent. Comput. Inf. Sci.

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“…Apart from that, applying machine learning techniques in medical applications could raise security and privacy issues, Weizhi discussed the CPS (Cyber‐physical System) attacks in the medical domains and proposed intrusion detection approach based on the behavioral profiling which shows a high performance. Machine learning has also been highly practically integrated in medical applications; a framework to avoid patients' private information leakage has been proposed based on the double‐blinding technique . For meeting the various of the medical data application, a new Naive Bayes classifier is set by Li et al, which could meet the requirements of the diverse private data.…”

Section: Related Workmentioning

confidence: 99%

Machine learning–based haptic‐enabled surgical navigation with security awareness

Tai

Wei

Zhou

et al. 2018

Concurrency and Computation

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Summary A novel security awareness surgical navigation system has been proposed for the accurate minimally invasive surgery with machine learning algorithms, haptic‐enabled devices, and customized surgical tools to guide the surgery with real‐time force and visual navigation. To provide a direct and simplified user interface during the operation, we combined traditional surgical guide images with AR‐based view and implemented a 3D reconstructed patient‐specific surgical environment includes with all surgical requisite details. In particular, we trained the surgical collected biomechanics haptic data by employed LSTM‐based RNN algorithm, and residual network for the intraoperative force manipulation prediction and classification, respectively. Experiments evaluation results on percutaneous therapy surgery demonstrated a higher performance and distinguished accuracy by the visual and haptic combined than the traditional navigation system. These preliminary study findings may suggested a new framework in the minimally invasive surgical navigation application and hint at the possibility integration of haptic, AR, and machine learning algorithms implementation in medical simulation. In addition, we take security into account when implementation this new framework.

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“…Using information gained from the IoT could make the environment around us be better cognized [4]. On the other hand, the IoT consists of devices that generate, process, and exchange vast amounts of critical security and safety data as well as privacy-sensitive information and hence are appealing targets for cyberattacks [5][6][7][8]. The task of affordably supporting security and privacy is quite challenging because many new networkable devices, which constitute the IoT, require less energy, are lightweight and have less memory [9].…”

Section: Introductionmentioning

confidence: 99%

Blockchain Based Credibility Verification Method for IoT Entities

Tao

Zhang

et al. 2018

Security and Communication Networks

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With the fast development of mobile Internet, Internet of Things (IoT) has been found in many important applications recently. However, it still faces many challenges in security and privacy. Blockchain (BC) technology, which underpins the cryptocurrency Bitcoin, has played an important role in the development of decentralized and data intensive applications running on millions of devices. In this paper, to establish the relationship between IoT and BC for device credibility verification, we propose a framework with layers, intersect, and self-organization Blockchain Structures (BCS). In this new framework, each BCS is organized by Blockchain technology. We describe the credibility verification method and show how it provide the verification. The efficiency and security analysis are also given in this paper, including its response time, storage efficiency, and verification. The conducted experiments have been shown to demonstrate the validity of the proposed method in satisfying the credible requirement achieved by Blockchain technology and certain advantages in storage space and response time.

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Privacy-preserving Naive Bayes classifiers secure against the substitution-then-comparison attack

Cited by 172 publications

References 22 publications

An information-aware visualization for privacy-preserving accelerometer data sharing

An information-aware visualization for privacy-preserving accelerometer data sharing

Machine learning–based haptic‐enabled surgical navigation with security awareness

Blockchain Based Credibility Verification Method for IoT Entities

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