Disclosure risk of individuals: A k-anonymity study on health care data related to Indian population

Panackal, Jisha Jose; Pillai, Anitha S.; Krishnachandran, V. N.

doi:10.1109/icdse.2014.6974637

Cited by 9 publications

(5 citation statements)

References 8 publications

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“…It is clear from the table II that the classifier accuracy is al- (1) bayes.NaiveBayes (2) rules.ZeroR (3) trees.RandomForest…”

Section: Comparison On Classification Accuracymentioning

confidence: 94%

“…The proposed framework described in figure 1 is being developed for solving the problem of disclosure risk as we have mentioned in [1]. Initially, we have developed a framework to solve the problem in NFHS-3, later we have generalized the framework which becomes adaptive to accommodate any data set.…”

Section: Proposed Frameworkmentioning

confidence: 99%

“…Hence, the need for the development of reliable and robust anonymization techniques for data, especially relating to health care data has become essential. The study [1], discussed the need to develop such a model for Privacy Preserving Data Mining by illustrating a detailed analysis on actual data related to Indian population namely, NFHS-3. Later, the study [2], proposed an algorithm called AUA (Adaptive Utility-based Anonymization) which provides a solution to the problem of disclosure risk.…”

Section: Introductionmentioning

confidence: 99%

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An intelligent framework for protecting privacy of individuals empirical evaluations on data mining classification

Panackal

Pillai

2014

2014 14th International Conference on Hybrid Intelligent Systems

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Along with rapid technological advancements, the need for developing suitable frameworks for protecting privacy of individuals becomes essential for the wide-spread acceptance of knowledge-based applications. Privacy Preserving Data Mining has become an active area of research recently to address privacy issues whenever the data is to be provided for a variety of purposes like survey, research etc. Several remarkable frameworks are being developed, but there is not enough sensible solution for considering both privacy and information evenly. Privacy mechanisms which compromise

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“…It is clear from the table II that the classifier accuracy is al- (1) bayes.NaiveBayes (2) rules.ZeroR (3) trees.RandomForest…”

Section: Comparison On Classification Accuracymentioning

confidence: 94%

Section: Proposed Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An intelligent framework for protecting privacy of individuals empirical evaluations on data mining classification

Panackal

Pillai

2014

2014 14th International Conference on Hybrid Intelligent Systems

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“…With the increasing complexity in data usage, however, it is not always easy to distinguish the sensitivity of data items, and thus it is impossible to rely on the removal of ''sensitive information". Meanwhile, employing approaches such as k-anonymity and its deviations to preserve data through generalising and suppressing the ''quasi-identifier" attributes so as to satisfy the mathematical model (Sweeney, 2002;Machanavajjhala et al, 2007;Li et al, 2007;Panackal et al, 2014). With these methods, data can be deidentified by generalising the attribute values against numerical requirements.…”

Section: Data Anonymitymentioning

confidence: 99%

Semantic privacy-preserving framework for electronic health record linkage

Lü

Sinnott

2018

Telematics and Informatics

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“…K-anonymity means that the quasi-identifiers of every record in a database are related to no fewer than k users. Many applications and techniques [30][31][32][33][34][35] are proposed to achieve k-anonymity. A paper [31] proposes an application of k-anonymity to preserve privacy in wireless sensor network medical environments.…”

Section: Pseudonymization Based Schemesmentioning

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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Disclosure risk of individuals: A k-anonymity study on health care data related to Indian population

Cited by 9 publications

References 8 publications

An intelligent framework for protecting privacy of individuals empirical evaluations on data mining classification

An intelligent framework for protecting privacy of individuals empirical evaluations on data mining classification

Semantic privacy-preserving framework for electronic health record linkage

Privacy-preserving disease detection techniques in e-healthcare systems

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