Publishing data from electronic health records while preserving privacy: A survey of algorithms

Gkoulalas-Divanis, Aris; Loukides, Grigorios; Sun, Jimeng

doi:10.1016/j.jbi.2014.06.002

Cited by 178 publications

(118 citation statements)

References 94 publications

Supporting

Mentioning

113

Contrasting

Unclassified

Order By: Relevance

“…In this section, we discuss anonymization methods that are closer to ours (see [15,52,25] for surveys). For extensive surveys on anonymization principles and methods for healthcare data, the reader is referred to [17,25,27].…”

Section: Related Workmentioning

confidence: 99%

“…In particular, the privacy-preserving sharing of healthcare data beyond authorized recipients (e.g., researchers or employees of the institution that has collected the data) is challenging [15,16,25]. This is partly because it cannot be facilitated based on access control and encryption-based methods [59,65], or by relying solely on policies (e.g., the HIPAA Privacy Rule [57] in the US, the Anonymization Code [56] in the UK, and the Data Protection Directive [58] in the EU).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Anonymizing datasets with demographics and diagnosis codes in the presence of utility constraints

Poulis

Loukides

Skiadopoulos

et al. 2017

Journal of Biomedical Informatics

Self Cite

View full text Add to dashboard Cite

Publishing data about patients that contain both demographics and diagnosis codes is essential to perform large-scale, low-cost medical studies. However, preserving the privacy and utility of such data is challenging, because it requires: (i) guarding against identity disclosure (re-identification) attacks based on both demographics and diagnosis codes, (ii) ensuring that the anonymized data remain useful in intended analysis tasks, and (iii) minimizing the information loss, incurred by anonymization, to preserve the utility of general analysis tasks that are difficult to determine before data publishing. Existing anonymization approaches are not suitable for being used in this setting, because they cannot satisfy all three requirements. Therefore, in this work, we propose a new approach to deal with this problem. We enforce the requirement (i) by applying (k, k m )-anonymity, a privacy principle that prevents re-identification from attackers who know the demographics of a patient and up to m of their diagnosis codes, where k and m are tunable parameters. To capture the requirement (ii), we propose the concept of utility constraint for both demographics and diagnosis codes. Utility constraints limit the amount of generalization and are specified by data owners (e.g., the healthcare institution that performs anonymization). We also capture requirement (iii), by employing well-established information loss measures for demographics and for diagnosis codes. To realize our approach, we develop an algorithm that enforces (k, k m )-anonymity on a dataset containing both demographics and diagnosis codes, in a way that satisfies the specified utility constraints and with minimal information loss, according to the measures. Our experiments with a large dataset containing more than 200, 000 electronic health records show the effectiveness and efficiency of our algorithm.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Anonymizing datasets with demographics and diagnosis codes in the presence of utility constraints

Poulis

Loukides

Skiadopoulos

et al. 2017

Journal of Biomedical Informatics

Self Cite

View full text Add to dashboard Cite

show abstract

“…El Emam and colleagues authored a good overview of anonymization [51]. A good detailed review of anonymization algorithms was authored by Gkoulalas-Divanis and colleagues [87]. Recent algorithms have focused on enhancing the utility of anonymized data [88][89][90] and applying anonymization to distributed data networks [91].…”

Section: Impact On Reusementioning

confidence: 99%

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Meystre¹,

Lovis²,

Bürkle³

et al. 2017

Yearb Med Inform

View full text Add to dashboard Cite

SummaryObjective: To perform a review of recent research in clinical data reuse or secondary use, and envision future advances in this field. Methods: The review is based on a large literature search in MEDLINE (through PubMed), conference proceedings, and the ACM Digital Library, focusing only on research published between 2005 and early 2016. Each selected publication was reviewed by the authors, and a structured analysis and summarization of its content was developed. Results: The initial search produced 359 publications, reduced after a manual examination of abstracts and full publications. The following aspects of clinical data reuse are discussed: motivations and challenges, privacy and ethical concerns, data integration and interoperability, data models and terminologies, unstructured data reuse, structured data mining, clinical practice and research integration, and examples of clinical data reuse (quality measurement and learning healthcare systems). Conclusion: Reuse of clinical data is a fast-growing field recognized as essential to realize the potentials for high quality healthcare, improved healthcare management, reduced healthcare costs, population health management, and effective clinical research.

show abstract

“…Most of the existing literature on automatically identifying sensitive information has addressed the task of masking personal data [2,5]. However, sensitive information in government documents is more wide-ranging than personal information and can include, for example, issues of con dentiality or international relations.…”

Section: Related Workmentioning

confidence: 99%

A Study of SVM Kernel Functions for Sensitivity Classification Ensembles with POS Sequences

McDonald

García-Pedrajas

Macdonald

et al. 2017

Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

View full text Add to dashboard Cite

Freedom of Information (FOI) laws legislate that government documents should be opened to the public. However, many government documents contain sensitive information, such as con dential information, that is exempt from release. erefore, government documents must be sensitivity reviewed prior to release, to identify and close any sensitive information. With the adoption of born-digital documents, such as email, there is a need for automatic sensitivity classi cation to assist digital sensitivity review. SVM classi ers and Part-of-Speech sequences have separately been shown to be promising for sensitivity classi cation. However, sequence classication methodologies, and speci cally SVM kernel functions, have not been fully investigated for sensitivity classi cation. erefore, in this work, we present an evaluation of ve SVM kernel functions for sensitivity classi cation using POS sequences. Moreover, we show that an ensemble classi er that combines POS sequence classi cation with text classi cation can signi cantly improve sensitivity classi cation e ectiveness (+6.09% F 2 ) compared with a text classi cation baseline, according to McNemar's test of signi cance.

show abstract

Publishing data from electronic health records while preserving privacy: A survey of algorithms

Cited by 178 publications

References 94 publications

Anonymizing datasets with demographics and diagnosis codes in the presence of utility constraints

Anonymizing datasets with demographics and diagnosis codes in the presence of utility constraints

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

A Study of SVM Kernel Functions for Sensitivity Classification Ensembles with POS Sequences

Contact Info

Product

Resources

About