Improved privacy preserving method for periodical SRS publishing

Huang, Wei; Yi, Tong; Zhu, Haibin; Shang, Wenqian; Lin, Weiguo

doi:10.1371/journal.pone.0250457

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…Besides medical data, similar methods can be exploited to release other kinds of data and provide protection for both the value and sensitivity. As a future perspective, we aim to extend Spreading to the increment medical data publishing [41][42][43][44]. Research towards this direction may discover sensitivity protection methods for Spontaneous reporting system data.…”

Section: Discussionmentioning

confidence: 99%

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Medical data publishing based on average distribution and clustering

Shi

Zhu

2022

CAAI Trans on Intel Tech

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Most of the data publishing methods have not considered sensitivity protection, and hence the adversary can disclose privacy by sensitivity attack. Faced with this problem, this paper presents a medical data publishing method based on sensitivity determination. To protect the sensitivity, the sensitivity of disease information is determined by semantics. To seek the trade‐off between information utility and privacy security, the new method focusses on the protection of sensitive values with high sensitivity and assigns the highly sensitive disease information to groups as evenly as possible. The experiments are conducted on two real‐world datasets, of which the records include various attributes of patients. To measure sensitivity protection, the authors define a metric, which can evaluate the degree of sensitivity disclosure. Besides, additional information loss and discernability metrics are used to measure the availability of released tables. The experimental results indicate that the new method can provide better privacy than the traditional one while the information utility is guaranteed. Besides value protection, the proposed method can provide sensitivity protection and available releasing for medical data.

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Section: Discussionmentioning

confidence: 99%

“…As a future perspective, we aim to extend Spreading to the increment medical data publishing [41–44]. Research towards this direction may discover sensitivity protection methods for Spontaneous reporting system data.…”

Section: Discussionmentioning

confidence: 99%

Medical data publishing based on average distribution and clustering

Shi

Zhu

2022

CAAI Trans on Intel Tech

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“…Based on our work [ 35 ], Huang et al [ 36 ] proposed 2 new attacks, MD -attack (Medicine Discontinuation attack) and SS -attack (Substantial Symptom attack). MD -attack assumes the attacker knew when the target stopped his/her treatment, that is, the quarter in which the target’s follow-up record discontinues, while SS -attack regards a QID group with a substantial amount of adverse reactions risky.…”

Section: Discussionmentioning

confidence: 99%

Privacy-Preserving Anonymity for Periodical Releases of Spontaneous Adverse Drug Event Reporting Data: Algorithm Development and Validation

Wang¹,

Lin²

2021

JMIR Med Inform

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Background Spontaneous reporting systems (SRSs) have been increasingly established to collect adverse drug events for fostering adverse drug reaction (ADR) detection and analysis research. SRS data contain personal information, and so their publication requires data anonymization to prevent the disclosure of individuals’ privacy. We have previously proposed a privacy model called MS(k, θ*)-bounding and the associated MS-Anonymization algorithm to fulfill the anonymization of SRS data. In the real world, the SRS data usually are released periodically (eg, FDA Adverse Event Reporting System [FAERS]) to accommodate newly collected adverse drug events. Different anonymized releases of SRS data available to the attacker may thwart our single-release-focus method, that is, MS(k, θ*)-bounding. Objective We investigate the privacy threat caused by periodical releases of SRS data and propose anonymization methods to prevent the disclosure of personal privacy information while maintaining the utility of published data. Methods We identify potential attacks on periodical releases of SRS data, namely, BFL-attacks, mainly caused by follow-up cases. We present a new privacy model called PPMS(k, θ*)-bounding, and propose the associated PPMS-Anonymization algorithm and 2 improvements: PPMS+-Anonymization and PPMS++-Anonymization. Empirical evaluations were performed using 32 selected FAERS quarter data sets from 2004Q1 to 2011Q4. The performance of the proposed versions of PPMS-Anonymization was inspected against MS-Anonymization from some aspects, including data distortion, measured by normalized information loss; privacy risk of anonymized data, measured by dangerous identity ratio and dangerous sensitivity ratio; and data utility, measured by the bias of signal counting and strength (proportional reporting ratio). Results The best version of PPMS-Anonymization, PPMS++-Anonymization, achieves nearly the same quality as MS-Anonymization in both privacy protection and data utility. Overall, PPMS++-Anonymization ensures zero privacy risk on record and attribute linkage, and exhibits 51%-78% and 59%-82% improvements on information loss over PPMS+-Anonymization and PPMS-Anonymization, respectively, and significantly reduces the bias of ADR signal. Conclusions The proposed PPMS(k, θ*)-bounding model and PPMS-Anonymization algorithm are effective in anonymizing SRS data sets in the periodical data publishing scenario, preventing the series of releases from disclosing personal sensitive information caused by BFL-attacks while maintaining the data utility for ADR signal detection.

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Abroad experience of electronic jurisdiction

Kanyuka

2023

Aktual. probl. pravozn.

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Improved privacy preserving method for periodical SRS publishing

Cited by 3 publications

References 24 publications

Medical data publishing based on average distribution and clustering

Medical data publishing based on average distribution and clustering

Privacy-Preserving Anonymity for Periodical Releases of Spontaneous Adverse Drug Event Reporting Data: Algorithm Development and Validation

Abroad experience of electronic jurisdiction

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