Biomedical data privacy: problems, perspectives, and recent advances

Malin, Bradley; Emam, Khaled El; O'Keefe, Christine M.

doi:10.1136/amiajnl-2012-001509

Cited by 118 publications

(65 citation statements)

References 57 publications

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“…The rapid development of data collection and management technologies has led to previously unknown challenges in that medical information is now available on mobile devices, shared networks and even sensors attached to the human body [70].…”

Section: Legal Issues Concerning Ehr and Hiementioning

confidence: 99%

Electronic health records, adoption, quality of care, legal and privacy issues and their implementation in emergency departments

Ben‐Assuli¹

2015

Health Policy

129

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Section: Legal Issues Concerning Ehr and Hiementioning

confidence: 99%

Electronic health records, adoption, quality of care, legal and privacy issues and their implementation in emergency departments

Ben‐Assuli¹

2015

Health Policy

129

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“…Access to and the use of patient data for research raise significant privacy concerns for the various stakeholders (i.e., patients and data custodians) [7, 13, 14]. A recent approach that addresses privacy concerns is secure multi-party computation (SMC), which deals with the problem of computing a function f on distributed data without revealing any information except the results.…”

Section: Introductionmentioning

confidence: 99%

Secure and scalable deduplication of horizontally partitioned health data for privacy-preserving distributed statistical computation

Yigzaw

Michalas

Bellika

2017

BMC Med Inform Decis Mak

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BackgroundTechniques have been developed to compute statistics on distributed datasets without revealing private information except the statistical results. However, duplicate records in a distributed dataset may lead to incorrect statistical results. Therefore, to increase the accuracy of the statistical analysis of a distributed dataset, secure deduplication is an important preprocessing step.MethodsWe designed a secure protocol for the deduplication of horizontally partitioned datasets with deterministic record linkage algorithms. We provided a formal security analysis of the protocol in the presence of semi-honest adversaries. The protocol was implemented and deployed across three microbiology laboratories located in Norway, and we ran experiments on the datasets in which the number of records for each laboratory varied. Experiments were also performed on simulated microbiology datasets and data custodians connected through a local area network.ResultsThe security analysis demonstrated that the protocol protects the privacy of individuals and data custodians under a semi-honest adversarial model. More precisely, the protocol remains secure with the collusion of up to N − 2 corrupt data custodians. The total runtime for the protocol scales linearly with the addition of data custodians and records. One million simulated records distributed across 20 data custodians were deduplicated within 45 s. The experimental results showed that the protocol is more efficient and scalable than previous protocols for the same problem.ConclusionsThe proposed deduplication protocol is efficient and scalable for practical uses while protecting the privacy of patients and data custodians.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-016-0389-x) contains supplementary material, which is available to authorized users.

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“…Advances in medical informatics have also raised new privacy issues, as information is gathered from sensors attached to the body or collected with mobile devices and shared easily with providers, insurers, employers and patients (Malin, Emam, & O'Keefe, 2013). The ready availability of information has great potential for improving health care, but it also sometimes leads patients to lose trust in their providers and forego treatment or seek help outside the system, especially for mental health issues and other stigmatised conditions.…”

Section: Journal Of Experimental and Theoretical Artificial Intelligencmentioning

confidence: 98%

The path to more general artificial intelligence

Goertzel

2014

Journal of Experimental & Theoretical Artificial Intelligen

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A small-N comparative analysis of six different areas of applied artificial intelligence (AI) suggests that the next period of development will require a merging of narrow-AI and strong-AI approaches. This will be necessary as programmers seek to move beyond developing narrowly defined tools to developing software agents capable of acting independently in complex environments. The present stage of artificial intelligence development is propitious for this because of the exponential increases in computer power and in available data streams over the last 25 years, and because of better understanding of the complex logic of intelligence. Applied areas chosen for examination were heart pacemakers, socialist economic planning, computer-based trading, self-driving automobiles, surveillance and sousveillance and artificial intelligence in medicine.

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Biomedical data privacy: problems, perspectives, and recent advances

Cited by 118 publications

References 57 publications

Electronic health records, adoption, quality of care, legal and privacy issues and their implementation in emergency departments

Electronic health records, adoption, quality of care, legal and privacy issues and their implementation in emergency departments

Secure and scalable deduplication of horizontally partitioned health data for privacy-preserving distributed statistical computation

The path to more general artificial intelligence

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