Application of Differential Privacy Approach in Healthcare Data – A Case Study

Zia, Muhammad Talha; Khan, Manzoor Ahmed; El-Sayed, Hesham

doi:10.1109/iit50501.2020.9299084

Cited by 8 publications

(4 citation statements)

References 10 publications

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“…Differential privacy captures the increased risk to the privacy of an individual incurred by participating in a database and often provides extremely accurate information about the database while simultaneously maintaining decently high levels of privacy. Studies that already applied differential privacy on electronic health records (EHR) and public health surveillance data generated through record linkage should be referenced [31,32].…”

Section: Ethical Considerations Regarding Fairnessmentioning

confidence: 99%

Ethical Considerations for Artificial Intelligence Applications for HIV

Garett,

Kim,

Young

2024

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Human Immunodeficiency Virus (HIV) is a stigmatizing disease that disproportionately affects African Americans and Latinos among people living with HIV (PLWH). Researchers are increasingly utilizing artificial intelligence (AI) to analyze large amounts of data such as social media data and electronic health records (EHR) for various HIV-related tasks, from prevention and surveillance to treatment and counseling. This paper explores the ethical considerations surrounding the use of AI for HIV with a focus on acceptability, trust, fairness, and transparency. To improve acceptability and trust towards AI systems for HIV, informed consent and a Federated Learning (FL) approach are suggested. In regard to unfairness, stakeholders should be wary of AI systems for HIV further stigmatizing or even being used as grounds to criminalize PLWH. To prevent criminalization, in particular, the application of differential privacy on HIV data generated by data linkage should be studied. Participatory design is crucial in designing the AI systems for HIV to be more transparent and inclusive. To this end, the formation of a data ethics committee and the construction of relevant frameworks and principles may need to be concurrently implemented. Lastly, the question of whether the amount of transparency beyond a certain threshold may overwhelm patients, thereby unexpectedly triggering negative consequences, is posed.

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Section: Ethical Considerations Regarding Fairnessmentioning

confidence: 99%

Ethical Considerations for Artificial Intelligence Applications for HIV

Garett,

Kim,

Young

2024

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“…DP is employed to prevent the leakage of personal information during the stage in a deep learning model [40][41][42]. Moreover, because healthcare data contain privacy-sensitive information, DP is adopted in various deep learning and artificial intelligence systems for healthcare system [43][44][45][46][47].…”

Section: Local Differential Privacy For De-identificationmentioning

confidence: 99%

Blockchain-Based Privacy-Preserving System for Genomic Data Management Using Local Differential Privacy

2021

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The advances made in genome technology have resulted in significant amounts of genomic data being generated at an increasing speed. As genomic data contain various privacy-sensitive information, security schemes that protect confidentiality and control access are essential. Many security techniques have been proposed to safeguard healthcare data. However, these techniques are inadequate for genomic data management because of their large size. Additionally, privacy problems due to the sharing of gene data are yet to be addressed. In this study, we propose a secure genomic data management system using blockchain and local differential privacy (LDP). The proposed system employs two types of storage: private storage for internal staff and semi-private storage for external users. In private storage, because encrypted gene data are stored, only internal employees can access the data. Meanwhile, in semi-private storage, gene data are irreversibly modified by LDP. Through LDP, different noises are added to each section of the genomic data. Therefore, even though the third party uses or exposes the shared data, the owner’s privacy is guaranteed. Furthermore, the access control for each storage is ensured by the blockchain, and the gene owner can trace the usage and sharing status using a decentralized application in a mobile device.

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“…In recent years, much literature has focused on homomorphic encryption algorithms [4][5][6][7][8][9]. Differential attack [6,7] is a main attack scenario in homomorphic aggregation.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Data Utility for a Differential Privacy Scheme Supporting Fault Tolerance

Zhang,

Wang,

Xiu

2023

Symmetry

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The evolution of smart grids improves the sustainability, controllability, stability, and efficiency of traditional power grids. There is a challenging issue in smart grids with protecting users’ privacy while collecting and controlling individual fine-grained data. To ensure data integrity and address the privacy issue, differential privacy protection is an efficient method to resist differential attacks on aggregated data. However, due to differential noise and faulty smart meters, the problem of differential noise deviation has a great impact on the utility of aggregated data. In this paper, we further supplement the previous work by improving the prediction method, forming a relatively complete DP protection scheme (DPP-UFT) with fault tolerance, and providing a detailed performance evaluation process. The experimental results show that the proposed method of adding differential noise based on the estimated failure rate is related to the estimated failure rate and the noise factor. Compared with several other related literature, it has achieved a higher data utilization effect.

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Application of Differential Privacy Approach in Healthcare Data – A Case Study

Cited by 8 publications

References 10 publications

Ethical Considerations for Artificial Intelligence Applications for HIV

Ethical Considerations for Artificial Intelligence Applications for HIV

Blockchain-Based Privacy-Preserving System for Genomic Data Management Using Local Differential Privacy

Performance Evaluation of Data Utility for a Differential Privacy Scheme Supporting Fault Tolerance

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