A survey of confidential data storage and deletion methods

Diesburg, Sarah; Wang, An-I Andy

doi:10.1145/1824795.1824797

Cited by 66 publications

(23 citation statements)

References 26 publications

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“…The method involves introducing appropriate number of errors in the dat a and control bits of the blocks of data making data recovery impossible. S. M. Diesburg, et al, summarized and compared existing methods of providing confidential storage and deletion of data in personal computing environments [6]. Paper [8] shows that these systems provide no temporal guarantees on data deletion and that deleted data still persists for nearly 44 hours with average phone use and indefinitely if the phone is not used after the deletion.…”

Section: Related Workmentioning

confidence: 99%

Secure Data Sanitization for Android Device Users

Huang¹,

He²,

Zhao³

2015

IJSIA

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To protect the data confidentiality of Android device users, this paper proposed a user-level secure data sanitization method which can make sensitive data stored in flash memory unrecoverable. The proposed method using reversed-scanning algorithm to reduce time and it don't need overwriting or erase the whole flash memory. After sensitive data pages are found, the program targetedly overwriting some blocks, erase them, and insert them into free block list waiting to be located again which means the blocks will be twice overwrited by new data.

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Section: Related Workmentioning

confidence: 99%

Secure Data Sanitization for Android Device Users

Huang¹,

He²,

Zhao³

2015

IJSIA

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“…For example, more than 96% of 2700 patient records that are involved in an NIH-funded GWAS and expected to be submitted into dbGaP were shown to be uniquely re-identifiable based on their diagnosis codes [11]. At the same time, this attack is not addressed by access control and encryption-based methods [14]- [16], as data need to be shared beyond authorized recipients.…”

Section: A Motivationmentioning

confidence: 99%

Utility-Aware Anonymization of Diagnosis Codes

Loukides

Gkoulalas-Divanis

2013

IEEE J. Biomed. Health Inform.

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The growing need for performing large-scale and low-cost biomedical studies has led organizations to promote the reuse of patient data. For instance, the National Institutes of Health in the US requires patient-specific data collected and analyzed in the context of Genome-Wide Association Studies (GWAS) to be deposited into a biorepository and broadly disseminated. While essential to comply with regulations, disseminating such data risks privacy breaches, because patients genomic sequences can be linked to their identities through diagnosis codes. This work proposes a novel approach that prevents this type of data linkage by modifying diagnosis codes to limit the probability of associating a patients identity to their genomic sequence. Our approach employs an effective algorithm that uses generalization and suppression of diagnosis codes to preserve privacy and takes into account the intended uses of the disseminated data to guarantee utility. We also present extensive experiments using several datasets derived from the Electronic Medical Record (EMR) system of the Vanderbilt University Medical Center, as well as a large-scale case-study using the EMRs of 79K patients, which are linked to DNA contained in the Vanderbilt University biobank. Our results verify that our approach generates anonymized data that permit accurate biomedical analysis in tasks including case count studies and GWAS.

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“…In this section, we survey research related to privacy-preserving data publishing, with a focus on biomedical data. We note that the reidentification problem is not addressed by access control and encryption-based methods [27]–[29] because data need to be shared beyond a small number of authorized recipients.…”

Section: Related Researchmentioning

confidence: 99%

Anonymization of Longitudinal Electronic Medical Records

Tamersoy

Loukides

Nergiz

et al. 2012

IEEE Trans. Inform. Technol. Biomed.

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Electronic medical record (EMR) systems have enabled healthcare providers to collect detailed patient information from the primary care domain. At the same time, longitudinal data from EMRs are increasingly combined with biorepositories to generate personalized clinical decision support protocols. Emerging policies encourage investigators to disseminate such data in a deidentified form for reuse and collaboration, but organizations are hesitant to do so because they fear such actions will jeopardize patient privacy. In particular, there are concerns that residual demographic and clinical features could be exploited for reidentification purposes. Various approaches have been developed to anonymize clinical data, but they neglect temporal information and are, thus, insufficient for emerging biomedical research paradigms. This paper proposes a novel approach to share patient-specific longitudinal data that offers robust privacy guarantees, while preserving data utility for many biomedical investigations. Our approach aggregates temporal and diagnostic information using heuristics inspired from sequence alignment and clustering methods. We demonstrate that the proposed approach can generate anonymized data that permit effective biomedical analysis using several patient cohorts derived from the EMR system of the Vanderbilt University Medical Center.

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A survey of confidential data storage and deletion methods

Cited by 66 publications

References 26 publications

Secure Data Sanitization for Android Device Users

Secure Data Sanitization for Android Device Users

Utility-Aware Anonymization of Diagnosis Codes

Anonymization of Longitudinal Electronic Medical Records

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