An efficient clustering-based anonymization scheme for privacy-preserving data collection in IoT based healthcare services

Andrew, J.; Karthikeyan, J.; Sei, Yuichi

doi:10.1007/s12083-021-01077-7

Cited by 54 publications

(24 citation statements)

References 74 publications

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“…It gives the percentage of information loss after the anonymization compared to the original attributes. To evaluate the performance of the proposed approach, NCP values from state-of-the-art anonymization approaches such as Mondrian [48], (k,km)- anonymity [52], and clustering-based k-anonymity [10]. We observed an increase in the information loss for a higher k value.…”

Section: B Experimental Analysismentioning

confidence: 92%

“…The Normalized Certainty Penalty (NCP) [59] was used as the standard information loss metric. The information loss is compared to Mondrian [48], (k,km)anonymity [52], and clustering-based k-anonymity [10], which are all prominent anonymization methods.…”

Section: ) Tuple Partitioningmentioning

confidence: 99%

“…Research on healthcare big data is exceptionally captious in improving the accuracy of diagnoses and developing ingenious medicines [5]- [7]. Generally, healthcare organizations share health records with research organizations for medical discoveries [8]- [10]. Organizations collect Electronic Health Records (EHRs) through the healthcare data process [11], which refers to the medical treatment of a patient in healthcare information technology (IT) ( e.g., Healthcare IoT Services).…”

Section: Introductionmentioning

confidence: 99%

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Privacy Preserving Attribute-Focused Anonymization Scheme for Healthcare Data Publishing

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Advancements in Industry 4.0 brought tremendous improvements in the healthcare sector, such as better quality of treatment, enhanced communication, remote monitoring, and reduced cost. Sharing healthcare data with healthcare providers is crucial for harnessing the benefits of such improvements. In general, healthcare data holds sensitive information about individuals. Hence, sharing such data is challenging because of various security and privacy issues. According to privacy regulations and ethical requirements, it is essential to preserve the privacy of patients before sharing data for medical research. State-of-the-art literature on privacy preserving studies either uses cryptographic approaches to protect the privacy or uses anonymizing techniques regardless of the type of attributes, this results in poor protection and data utility. In this paper, we propose an attribute-focused privacy preserving data publishing scheme. The proposed scheme is two-fold, comprising a fixed-interval approach to protect numerical attributes and an improved l-diverse slicing approach to protect the categorical and sensitive attributes. In the fixed-interval approach, the original values of the healthcare data are replaced with an equivalent computed value. The improved l-diverse slicing approach partitions the data both horizontally and vertically to avoid privacy leaks. Extensive experiments with real-world datasets are conducted to evaluate the performance of the proposed scheme. The classification models built on anonymized dataset yields approximately 13% better accuracy than benchmarked algorithms. Experimental analyses show that the average information loss which is measured by normalized certainty penalty (NCP) is reduced by 12% compared to similar approaches. The attribute focused scheme not only provides data utility but also prevents the data from membership disclosures, attribute disclosures, and identity disclosures.

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Section: B Experimental Analysismentioning

confidence: 92%

Section: ) Tuple Partitioningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Privacy Preserving Attribute-Focused Anonymization Scheme for Healthcare Data Publishing

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“…The wireless channels used for the transmission of the data pose security threats in the data; this can be solved using blockchain technology [45], [8], [26]. Blockchain technology uses a cryptographic hash function in providing the necessary security to the data [43], [14]. The blockchain implementations have bought significant impacts in providing security, enabling encryption techniques [1], [47].…”

Section: ) Secured Wireless Healthcare Network Using Blockchain Technologymentioning

confidence: 99%

Blockchain-based Intelligent Monitored Security System for Detection of Replication Attack in the Wireless Healthcare Network

Warraich

Singh

Thapa

2021

EJ-ENG

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Wireless sensor networks have revolutionized the way healthcare works replacing the traditional methods with sensor-enabled IoT devices that help in monitoring the data. The data is collected by these sensors that are there on the body of the user, the data is transmitted over the network to the healthcare monitoring systems. The transmission follows the route of the wireless channel that is not secure as it can be accessed by legitimate as well as illegitimate users. These pose security threats; one such attack is a replication attack. This makes the replicas of the original node, replaces the data with the malicious content for attacking the system, and deploys the node back to the network making it difficult to detect. The aim of the work is to review the Blockchain-based intelligent monitored security system for the detection of replication attacks in the wireless healthcare network. The method used for review is the secondary research method. The main focus of the work is kept on the literature review for obtaining insights and knowledge. The results show that blockchain provides the required security to the data carried by the sensor-enabled IoT. The result contributed to the understanding of the different blockchain techniques in securing data. The system component is farmed in the work and verified in the results.

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“…Their mechanism also assumed a central trusted server, although they partly used local differential privacy. Onesimu et al proposed a novel privacy protection data collection scheme for IoT-based healthcare service systems [25]. Their method uses a clustering-based anonymous model to develop an efficient privacy protection scheme that satisfies privacy requirements and protects a healthcare IoT from various privacy attacks.…”

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confidence: 99%

Private True Data Mining: Differential Privacy Featuring Errors to Manage Internet-of-Things Data

Sei

Ohsuga

2022

IEEE Access

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Available data may differ from true data in many cases due to sensing errors, especially for the Internet of Things (IoT). Although privacy-preserving data mining has been widely studied during the last decade, little attention has been paid to data values containing errors. Differential privacy, which is the de facto standard privacy metric, can be achieved by adding noise to a target value that must be protected. However, if the target value already contains errors, there is no reason to add extra noise. In this paper, a novel privacy model called true-value-based differential privacy (TDP) is proposed. This model applies traditional differential privacy to the "true value" unknown by the data owner or anonymizer but not to the "measured value" containing errors. Based on TDP, the amount of noise added by differential privacy techniques can be reduced by approximately 20% by our solution. As a result, the error of generated histograms can be reduced by 40.4% and 29.6% on average according to mean square error and Jensen-Shannon divergence, respectively.We validate this result on synthetic and five real data sets. Moreover, we proved that the privacy protection level does not decrease as long as the measurement error is not overestimated. INDEX TERMSData mining, Data privacy, Differentnial privacy, Internet of Things I. INTRODUCTION 1 Significant amounts of IoT data are generated every day by 2 many different sensors, such as thermal cameras, home appli-3 ance sensors, automotive sensors, and smartphone-equipped 4 sensors. These IoT data can be used for health monitoring [1], 5 context-aware recommendation (or recommender) systems 6 [2], navigation [3], and other applications.7 However, sensing people or their surrounding environment 8 might involve information that identifies an individual [4]. 9 Thus private information is at risk of leakage. By anonymiz-10 ing data based on ϵ-differential privacy [5], [6], which is the 11 de facto standard privacy metric (ϵ represents the privacy 12 budget), privacy leakage can be controlled. Differential pri-13 vacy has been used in many studies, such as [7]-[9], as it is 14 one of the most critical privacy metrics [10]. It is considered 15 an important concept for data analysis [11], [12]. 16 Local differential privacy is a specialized concept of dif-17 ferential privacy especially for data collection from each 18 person. In this paper, "differential privacy," refers to "lo-19

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An efficient clustering-based anonymization scheme for privacy-preserving data collection in IoT based healthcare services

Cited by 54 publications

References 74 publications

Privacy Preserving Attribute-Focused Anonymization Scheme for Healthcare Data Publishing

Privacy Preserving Attribute-Focused Anonymization Scheme for Healthcare Data Publishing

Blockchain-based Intelligent Monitored Security System for Detection of Replication Attack in the Wireless Healthcare Network

Private True Data Mining: Differential Privacy Featuring Errors to Manage Internet-of-Things Data

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