The Experts below are selected from a list of 2441028 Experts worldwide ranked by ideXlab platform
Jake Y Chen - One of the best experts on this subject based on the ideXlab platform.
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New threats to Health Data privacy
BMC Bioinformatics, 2011Co-Authors: Xukai Zou, Peng Liu, Jake Y ChenAbstract:Abstract Background Along with the rapid digitalization of Health Data (e.g. Electronic Health Records), there is an increasing concern on maintaining Data privacy while garnering the benefits, especially when the Data are required to be published for secondary use. Most of the current research on protecting Health Data privacy is centered around Data de-identification and Data anonymization, which removes the identifiable information from the published Health Data to prevent an adversary from reasoning about the privacy of the patients. However, published Health Data is not the only source that the adversaries can count on: with a large amount of information that people voluntarily share on the Web, sophisticated attacks that join disparate information pieces from multiple sources against Health Data privacy become practical. Limited efforts have been devoted to studying these attacks yet. Results We study how patient privacy could be compromised with the help of today’s information technologies. In particular, we show that private Healthcare information could be collected by aggregating and associating disparate pieces of information from multiple online Data sources including online social networks, public records and search engine results. We demonstrate a real-world case study to show user identity and privacy are highly vulnerable to the attribution, inference and aggregation attacks. We also show that people are highly identifiable to adversaries even with inaccurate information pieces about the target, with real Data analysis. Conclusion We claim that too much information has been made available electronic and available online that people are very vulnerable without effective privacy protection.
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New threats to Health Data privacy.
BMC bioinformatics, 2011Co-Authors: Xukai Zou, Peng Liu, Jake Y ChenAbstract:Along with the rapid digitalization of Health Data (e.g. Electronic Health Records), there is an increasing concern on maintaining Data privacy while garnering the benefits, especially when the Data are required to be published for secondary use. Most of the current research on protecting Health Data privacy is centered around Data de-identification and Data anonymization, which removes the identifiable information from the published Health Data to prevent an adversary from reasoning about the privacy of the patients. However, published Health Data is not the only source that the adversaries can count on: with a large amount of information that people voluntarily share on the Web, sophisticated attacks that join disparate information pieces from multiple sources against Health Data privacy become practical. Limited efforts have been devoted to studying these attacks yet. We study how patient privacy could be compromised with the help of today's information technologies. In particular, we show that private Healthcare information could be collected by aggregating and associating disparate pieces of information from multiple online Data sources including online social networks, public records and search engine results. We demonstrate a real-world case study to show user identity and privacy are highly vulnerable to the attribution, inference and aggregation attacks. We also show that people are highly identifiable to adversaries even with inaccurate information pieces about the target, with real Data analysis. We claim that too much information has been made available electronic and available online that people are very vulnerable without effective privacy protection.
Peter J. Diggle - One of the best experts on this subject based on the ideXlab platform.
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Applied Spatial Statistics for Public Health Data
Journal of the American Statistical Association, 2005Co-Authors: Peter J. DiggleAbstract:(2005). Applied Spatial Statistics for Public Health Data. Journal of the American Statistical Association: Vol. 100, No. 470, pp. 702-703.
Xukai Zou - One of the best experts on this subject based on the ideXlab platform.
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New threats to Health Data privacy
BMC Bioinformatics, 2011Co-Authors: Xukai Zou, Peng Liu, Jake Y ChenAbstract:Abstract Background Along with the rapid digitalization of Health Data (e.g. Electronic Health Records), there is an increasing concern on maintaining Data privacy while garnering the benefits, especially when the Data are required to be published for secondary use. Most of the current research on protecting Health Data privacy is centered around Data de-identification and Data anonymization, which removes the identifiable information from the published Health Data to prevent an adversary from reasoning about the privacy of the patients. However, published Health Data is not the only source that the adversaries can count on: with a large amount of information that people voluntarily share on the Web, sophisticated attacks that join disparate information pieces from multiple sources against Health Data privacy become practical. Limited efforts have been devoted to studying these attacks yet. Results We study how patient privacy could be compromised with the help of today’s information technologies. In particular, we show that private Healthcare information could be collected by aggregating and associating disparate pieces of information from multiple online Data sources including online social networks, public records and search engine results. We demonstrate a real-world case study to show user identity and privacy are highly vulnerable to the attribution, inference and aggregation attacks. We also show that people are highly identifiable to adversaries even with inaccurate information pieces about the target, with real Data analysis. Conclusion We claim that too much information has been made available electronic and available online that people are very vulnerable without effective privacy protection.
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New threats to Health Data privacy.
BMC bioinformatics, 2011Co-Authors: Xukai Zou, Peng Liu, Jake Y ChenAbstract:Along with the rapid digitalization of Health Data (e.g. Electronic Health Records), there is an increasing concern on maintaining Data privacy while garnering the benefits, especially when the Data are required to be published for secondary use. Most of the current research on protecting Health Data privacy is centered around Data de-identification and Data anonymization, which removes the identifiable information from the published Health Data to prevent an adversary from reasoning about the privacy of the patients. However, published Health Data is not the only source that the adversaries can count on: with a large amount of information that people voluntarily share on the Web, sophisticated attacks that join disparate information pieces from multiple sources against Health Data privacy become practical. Limited efforts have been devoted to studying these attacks yet. We study how patient privacy could be compromised with the help of today's information technologies. In particular, we show that private Healthcare information could be collected by aggregating and associating disparate pieces of information from multiple online Data sources including online social networks, public records and search engine results. We demonstrate a real-world case study to show user identity and privacy are highly vulnerable to the attribution, inference and aggregation attacks. We also show that people are highly identifiable to adversaries even with inaccurate information pieces about the target, with real Data analysis. We claim that too much information has been made available electronic and available online that people are very vulnerable without effective privacy protection.
Bonnie Kaplan - One of the best experts on this subject based on the ideXlab platform.
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Rethinking Health Data Privacy
2019Co-Authors: Bonnie Kaplan, Elizabeth J. Davidson, George Demiris, Richard Schreiber, Ari Ezra WaldmanAbstract:Privacy is protected both ethically and legally as a foundation for creating enough trust during clinical encounters for Health care decisions to be based on honest discussion and accurate information exchange. Research, public Health surveillance, advances in artificial intelligence (e.g., machine learning and predictive algorithms for Health care), and the promise of personalized medicine all depend on accurate, complete Data. However, Health-related Data generated and used outside of clinical settings is not protected through privacy regulation. Data aggregators and companies combine Data from multiple sources for Health and other purposes, while individual behavioral and social practices are being incorporated into medical records. Meanwhile, boundaries and distinctions are breaking down between different categories of protected Health Data, and between protected Data and Data collected via commercial apps and services. Privacy of Health-related Data requires rethinking in this rapidly changing landscape. Panelists will consider challenges in Health Data privacy, Data governance, and privacy policies and practices, including privacy vs the value of Data sharing, the adequacy of current legal and regulatory regimes, and how technological developments affect Health Data privacy.
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Selling Health Data: de-identification, privacy, and speech.
Cambridge quarterly of healthcare ethics : CQ : the international journal of healthcare ethics committees, 2015Co-Authors: Bonnie KaplanAbstract:Two court cases that involve selling prescription Data for pharmaceutical marketing affect biomedical informatics, patient and clinician privacy, and regulation. Sorrell v. IMS Health Inc. et al. in the United States and R v. Department of Health, Ex Parte Source Informatics Ltd. in the United Kingdom concern privacy and Health Data protection, Data de-identification and reidentification, drug detailing (marketing), commercial benefit from the required disclosure of personal information, clinician privacy and the duty of confidentiality, beneficial and unsavory uses of Health Data, regulating Health technologies, and considering Data as speech. Individuals should, at the very least, be aware of how Data about them are collected and used. Taking account of how those Data are used is needed so societal norms and law evolve ethically as new technologies affect Health Data privacy and protection.
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Selling Health Data: De-Identification, Privacy, and Speech
2014Co-Authors: Bonnie KaplanAbstract:Two court cases that involve selling prescription Data for pharmaceutical marketing affect biomedical informatics, patient and clinician privacy, and regulation. Sorrell v. IMS Health, Inc. et al. in the US and R v. Department of Health, Ex Parte Source Informatics Ltd in the UK concern privacy and Health Data protection, Data de-identification and re-identification, drug detailing (marketing), commercial benefit from required disclosure of personal information, clinician privacy and duty of confidentiality, beneficial and unsavory uses of Health Data, regulating Health technologies, and considering Data as speech. Individuals should, at the very least, be aware of how Data about them is collected and used. Taking account of how that Data is used is needed so societal norms and law evolve ethically as new technologies affect Health Data privacy and protection.
Bradley A Malin - One of the best experts on this subject based on the ideXlab platform.
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a systematic review of re identification attacks on Health Data
PLOS ONE, 2011Co-Authors: Khaled El Emam, Elizabeth Jonker, Luk Arbuckle, Bradley A MalinAbstract:Background: Privacy legislation in most jurisdictions allows the disclosure of Health Data for secondary purposes without patient consent if it is de-identified. Some recent articles in the medical, legal, and computer science literature have argued that de-identification methods do not provide sufficient protection because they are easy to reverse. Should this be the case, it would have significant and important implications on how Health information is disclosed, including: (a) potentially limiting its availability for secondary purposes such as research, and (b) resulting in more identifiable Health information being disclosed. Our objectives in this systematic review were to: (a) characterize known re-identification attacks on Health Data and contrast that to re-identification attacks on other kinds of Data, (b) compute the overall proportion of records that have been correctly re-identified in these attacks, and (c) assess whether these demonstrate weaknesses in current deidentification methods. Methods and Findings: Searches were conducted in IEEE Xplore, ACM Digital Library, and PubMed. After screening, fourteen eligible articles representing distinct attacks were identified. On average, approximately a quarter of the records were re-identified across all studies (0.26 with 95% CI 0.046–0.478) and 0.34 for attacks on Health Data (95% CI 0–0.744). There was considerable uncertainty around the proportions as evidenced by the wide confidence intervals, and the mean proportion of records re-identified was sensitive to unpublished studies. Two of fourteen attacks were performed with Data that was de-identified using existing standards. Only one of these attacks was on Health Data, which resulted in a success rate of 0.00013. Conclusions: The current evidence shows a high re-identification rate but is dominated by small-scale studies on Data that was not de-identified according to existing standards. This evidence is insufficient to draw conclusions about the efficacy of de-identification methods.
