Security, privacy, and trust management in DNA computing

Fernandes, Maria; Decouchant, Jérémie; Couto, Francisco M.

doi:10.1016/bs.adcom.2022.08.009

Cited by 3 publications

(1 citation statement)

References 90 publications

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“…Generative medical diagnosis and drug discovery AI involving genomic databases and chemical compound or protein structure databases are extremely susceptible to privacy attacks. Fernandes et al [87] pointed out that genomic data such as DNA data are susceptible to inference attacks, reidentification attacks, membership attacks, and recovery attacks. It is extremely concerning when such attacks target high-profile individuals.…”

Section: Implementation Phasementioning

confidence: 99%

Generative AI in Medical Practice: In-Depth Exploration of Privacy and Security Challenges

Chen,

Esmaeilzadeh

2024

J Med Internet Res

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As advances in artificial intelligence (AI) continue to transform and revolutionize the field of medicine, understanding the potential uses of generative AI in health care becomes increasingly important. Generative AI, including models such as generative adversarial networks and large language models, shows promise in transforming medical diagnostics, research, treatment planning, and patient care. However, these data-intensive systems pose new threats to protected health information. This Viewpoint paper aims to explore various categories of generative AI in health care, including medical diagnostics, drug discovery, virtual health assistants, medical research, and clinical decision support, while identifying security and privacy threats within each phase of the life cycle of such systems (ie, data collection, model development, and implementation phases). The objectives of this study were to analyze the current state of generative AI in health care, identify opportunities and privacy and security challenges posed by integrating these technologies into existing health care infrastructure, and propose strategies for mitigating security and privacy risks. This study highlights the importance of addressing the security and privacy threats associated with generative AI in health care to ensure the safe and effective use of these systems. The findings of this study can inform the development of future generative AI systems in health care and help health care organizations better understand the potential benefits and risks associated with these systems. By examining the use cases and benefits of generative AI across diverse domains within health care, this paper contributes to theoretical discussions surrounding AI ethics, security vulnerabilities, and data privacy regulations. In addition, this study provides practical insights for stakeholders looking to adopt generative AI solutions within their organizations.

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Section: Implementation Phasementioning

confidence: 99%

Generative AI in Medical Practice: In-Depth Exploration of Privacy and Security Challenges

Chen,

Esmaeilzadeh

2024

J Med Internet Res

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Survey on Recommender Systems for Biomedical Items in Life and Health Sciences

Pato,

Barros,

Couto

2024

ACM Comput. Surv.

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The generation of biomedical data is of such a magnitude that its retrieval and analysis have posed several challenges. A survey of recommender system (RS) approaches in biomedical fields is provided in this analysis, along with a discussion of existing challenges related to large-scale biomedical information retrieval systems. We collect original studies, identify entities, models, and how knowledge graphs (KG) can improve results. As a result, most of the papers used model-based collaborative filtering algorithms, most of the available datasets did not follow the standard format < user, item, rating >, and regarding qualitative evaluations of RSs use mainly classification metrics. Finally, we have assembled and coded a unique dataset of 60 papers — Sur-RS4BioT, available for download at DOI:10.34740/kaggle/ds/2346894

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Secure Multi-Party Computation (SMPC) Protocols and Privacy

Rahaman,

Arya,

Orozco

et al. 2024

Advances in Information Security, Privacy, and Ethics

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This chapter explores the growing field of secure multi-party computation (SMPC), an important part of modern cryptography that protects privacy in group computing tasks. It does this by looking at their architectural designs, security models, ways to make them more efficient, and cross-domain uses. It goes into detail about the complex architecture and cryptographic methods that support SMPC, such as homomorphic encryption and sharing secrets, which protect the privacy and integrity of data in computations involving more than one person. This part also talks about strong security models that can protect against a wide range of threats, as well as ways to make algorithmic design and protocol implementation more efficient. In addition, it talks about how SMPC can be used in many different areas, like healthcare, banking, and AI, and stresses the need to follow privacy rules like GDPR. The chapter ends with a look ahead, talking about new challenges and trends that are coming up.

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Security, privacy, and trust management in DNA computing

Cited by 3 publications

References 90 publications

Generative AI in Medical Practice: In-Depth Exploration of Privacy and Security Challenges

Generative AI in Medical Practice: In-Depth Exploration of Privacy and Security Challenges

Survey on Recommender Systems for Biomedical Items in Life and Health Sciences

Secure Multi-Party Computation (SMPC) Protocols and Privacy

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