Healthcare Data Security Technology: HIPAA Compliance

Mbonihankuye, Scholas; Nkunzimana, Athanase; Ndagijimana, Ange

doi:10.1155/2019/1927495

Cited by 42 publications

(16 citation statements)

References 13 publications

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“…Dataset size 2000 3000 4000 5000 6000 7000 8000 9000 9358 24.6 32.6 32.0 31.0 30.0 30.0 29.0 28.0 55. 3 Table 6: List of the lowest values obtained for each data size of the synthetic versions of the Health Survey dataset, using Gretel Synthetics.…”

Section: Data Synthesis Tools Assessmentmentioning

confidence: 99%

“…Differential privacy [1] (DP) has been proposed as a formal privacy guarantee for data. It reinforces a more substantial privacy basis for privacy regulations such as GDPR [2] or HIPAA [3] than existing approaches like anonymization, and is regarded as the most viable privacy protection technique for real-world data analysis [4]. Nevertheless, a gap exists between fundamental and applied research on the topic of DP [5].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Open-source Tools for Differential Privacy

Zhang¹,

Anton²,

Slavnic³

et al. 2022

Preprint

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The concept of differential privacy has widely penetrated academia and industry, with its formal guarantee on individual privacy that leads to compliances with privacy legislation, e.g., GDPR. However, there is a lack of understanding on tools capable of achieving differential privacy, and it is not clear what to expect from existing differential privacy tools when implementing privacy protection. Such an obstacle limits private applications' further prosperity. This paper reviews and evaluates the state-of-the-art open-source differential privacy tools of different domains using various estimating categories and privacy settings. Particularly, we look into the performances of three differential privacy tools for machine learning, two for statistical query, and four for synthetic data generation. We test all the tools on both continuous and categorical data and quantify their performance under different privacy budget and data size w.r.t. utility loss and system overhead. The accumulated evaluation results reveal several patterns that users can follow to optimally configure the tools, and provide preliminary guidelines on tool selection under different criteria. Finally, we openly release our evaluation coding repository, a framework that users can reuse to further evaluate the studied tools and beyond. We anticipate this work to provide a comprehensive insight into the performances of the existing dominant privacy tools, and a concrete reference for a potentially large developer community on private applications, thus narrowing the gap between conceptual differential privacy and private functionality development.

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Section: Data Synthesis Tools Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of Open-source Tools for Differential Privacy

Zhang¹,

Anton²,

Slavnic³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…CE's clients and ventures rely heavily on the types of help they provide. Various exploration endeavors and arrangements have been proposed to go to protection and security challenges [10][11][12][13].…”

Section: A Related Workmentioning

confidence: 99%

Health Information Sharing in Cloud Environment Using Modular Encryption Standard

Prabha¹,

Kanagasabapathi²,

Sajeeth³

et al. 2023

Advances in Transdisciplinary Engineering

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Despite the Cloud Environment’s (CE) numerous and obvious improvements in healthcare, these issues continue to impede CE’s development. In order to fully understand and utilise such issues, the utmost serious thinking is required. Global, national, and local health information are in demand. Setting up the required security protocols for handling security issues and breaches is essential for getting the most out of healthcare services. The Modular Encryption Standard (MES) is therefore being investigated for this research in order to provide requirement-based health data protection in light of the tiering of security efforts. According to the presentation evaluation, the suggested work outperforms other regularly used calculations against the security of health data in the CE environment in terms of enhanced performance and practical subjective security ensuring approaches.

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“…Fearing punishment and slander, many incidents went unreported. There were 9.1 million records reportedly exposed due to hacking and other IT issues [34] [10].…”

Section: Types Of Threats In Healthcarementioning

confidence: 99%

Applying the Modular Encryption Standard to Mobile Cloud Computing to Improve the Safety of Health Data

Kumar¹,

Siddardha²,

Reddy³

et al. 2022

Journal of Pharmaceutical Negative Results

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Mobile Cloud Computing (MCC) has numerous and easily observable benefits in healthcare, but its growth is being hampered by protection and security concerns. The problem at hand calls for one's whole attention and seriousness if one is to grasp its scope and make good use of it. A global, territorial, and local effort is required to disseminate health information. To completely profit of the wellbeing administrations, it is significant to set up the requested security rehearses for the counteraction of safety breaks and weaknesses.

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Healthcare Data Security Technology: HIPAA Compliance

Cited by 42 publications

References 13 publications

Evaluation of Open-source Tools for Differential Privacy

Evaluation of Open-source Tools for Differential Privacy

Health Information Sharing in Cloud Environment Using Modular Encryption Standard

Applying the Modular Encryption Standard to Mobile Cloud Computing to Improve the Safety of Health Data

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