Differential Privacy-Based Genetic Matching in Personalized Medicine

Wei, Jianhao; Yao, Lin; Yao, Xin; Zhang, Jin; Liu, Xinbo

doi:10.1109/tetc.2020.2970094

Cited by 15 publications

(3 citation statements)

References 36 publications

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“…Therefore, genetic data sharing will definitely improve the use of AI in the SHGP and Personalized Medicine. Furthermore, AI and privacy technologies could provide solutions for genetic data sharing, for example, cryptography, differential privacy and other approaches 23 , 24 , 38 , 39 .…”

Section: Discussionmentioning

confidence: 99%

Genetic data sharing and artificial intelligence in the era of personalized medicine based on a cross‐sectional analysis of the Saudi human genome program

Alrefaei

Hawsawi

Almaleki

et al. 2022

Sci Rep

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The success of the Saudi Human Genome Program (SHGP), one of the top ten genomic programs worldwide, is highly dependent on the Saudi population embracing the concept of participating in genetic testing. However, genetic data sharing and artificial intelligence (AI) in genomics are critical public issues in medical care and scientific research. The present study was aimed to examine the awareness, knowledge, and attitude of the Saudi society towards the SHGP, the sharing and privacy of genetic data resulting from the SHGP, and the role of AI in genetic data analysis and regulations. Results of a questionnaire survey with 804 respondents revealed moderate awareness and attitude towards the SHGP and minimal knowledge regarding its benefits and applications. Respondents demonstrated a low level of knowledge regarding the privacy of genetic data. A generally positive attitude was found towards the outcomes of the SHGP and genetic data sharing for medical and scientific research. The highest level of knowledge was detected regarding AI use in genetic data analysis and privacy regulation. We recommend that the SHGP’s regulators launch awareness campaigns and educational programs to increase and improve public awareness and knowledge regarding the SHGP’s benefits and applications. Furthermore, we propose a strategy for genetic data sharing which will facilitate genetic data sharing between institutions and advance Personalized Medicine in genetic diseases’ diagnosis and treatment.

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Section: Discussionmentioning

confidence: 99%

Genetic data sharing and artificial intelligence in the era of personalized medicine based on a cross‐sectional analysis of the Saudi human genome program

Alrefaei

Hawsawi

Almaleki

et al. 2022

Sci Rep

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“…Liu et al proposed a method for coronary heart disease diagnosis in mobile edge computing based on blockchain-enabled contextual online learning [127]. Wei et al made use of differential privacy to achieve genetic matching for personalized medicine [128]. Wang et al put forward a method for secure medical data collection based on local differential privacy [129].…”

Section: Industrial Bioengineeringmentioning

confidence: 99%

Differential Privacy for Industrial Internet of Things: Opportunities, Applications and Challenges

Jiang,

Li,

Yue

et al. 2021

Preprint

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The development of Internet of Things (IoT) brings new changes to various fields. Particularly, industrial Internet of Things (IIoT) is promoting a new round of industrial revolution. With more applications of IIoT, privacy protection issues are emerging. Specially, some common algorithms in IIoT technology such as deep models strongly rely on data collection, which leads to the risk of privacy disclosure. Recently, differential privacy has been used to protect user-terminal privacy in IIoT, so it is necessary to make in-depth research on this topic. In this paper, we conduct a comprehensive survey on the opportunities, applications and challenges of differential privacy in IIoT. We firstly review related papers on IIoT and privacy protection, respectively. Then we focus on the metrics of industrial data privacy, and analyze the contradiction between data utilization for deep models and individual privacy protection. Several valuable problems are summarized and new research ideas are put forward. In conclusion, this survey is dedicated to complete comprehensive summary and lay foundation for the follow-up researches on industrial differential privacy.

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“…This is done by adding noise to every database query, obfuscating the returned answer so that any modifications made are hidden. An implementation of differential privacy for use of genetic matching has previously been explored [134], however there is still room for further research into possible implementations for genetic privacy. Research and implementation of such PETs for the handling and storage of genetic information could allow individuals and their family members to retain their privacy, while simultaneously being given the choice to have their information used for purposes of their choice.…”

Section: Privacy Enhancing Technologiesmentioning

confidence: 99%

Privacy, Biological Relatives, and at-Home DNA Testing

Baig¹

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At-home DNA testing remains popular amongst individuals today. These direct-toconsumer services come with several privacy risks, that can extend far beyond the individuals taking the test. How do participants attribute risk to biological family members? How do users and non-users differ in comfort with their data being shared, and their understanding of privacy risks? How do privacy perceptions differ for ancestry and health data? To investigate these questions, we conducted a 2 × 2 survey, and discovered non-users were significantly more privacy conscious, and that health data was considered more beneficial overall. We then interviewed 10 biological family members of users who had not taken a test themselves; though many were unconcerned or indifferent towards privacy, privacy-conscious participants were frustrated by, and resigned to, the loss of control over their data. We discuss our findings, the implications of our research, offer recommendations to improve privacy, and identify areas for future research.This thesis is the result of support and guidance from many over the years. To my supervisor, Sonia Chiasson: I would never have found my love for research and HCI had I not stumbled across CHORUS as an undergraduate in 2015. You have my deepest gratitude, for the infinite patience, feedback, and guidance. You are a stellar mentor, and have always been there for your students (rain or shine). I feel privileged to have been mentored by you, and am looking forward to all the future research yet to come! (Also, have this:).To my committee members, Elizabeth Stobert and Hussein Al Osman: thank you for your insights and feedback which helped improve this thesis. I would also like to thank Alan Tsang for chairing the defence, and my participants for their time.To everyone at CHORUS: thank you for your feedback at practice sessions, listening to me talk about my thesis at every lab meeting, and for all the laughs.To my friends: FW, DN; the many work sessions (and non-work chats!) we did helped produce this thesis. Reham; I could not have done this without your emotional support. Thank you for all the hours where we sat together, had some chai (taye), and discussed various aspects of this research (and life). JS, HL, and all my friends; thank you for the many conversations and jokes that kept me smiling.To the Baig/Bru families here: thank you making sure I was fed, for the sweets, games, and visits. To my family back home; as I stressed about deadlines, you all stressed with me :). Nano; your songs gave my heart "thandak". Thank you all for the unwavering support, even as I was unavailable to meet or answer phone calls.James: thank you for your incredible patience, for staying up late with me, and giving me stellar graphic design advice. Knowing that I could turn to you for rubberducking and/or assistance, or a laugh and hug, made everything much easier.Baba: around ten years ago, you and mama made a difficult choice, and provided your children with an opportunity many don't have. Without you I would not be here, ...

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Differential Privacy-Based Genetic Matching in Personalized Medicine

Cited by 15 publications

References 36 publications

Genetic data sharing and artificial intelligence in the era of personalized medicine based on a cross‐sectional analysis of the Saudi human genome program

Genetic data sharing and artificial intelligence in the era of personalized medicine based on a cross‐sectional analysis of the Saudi human genome program

Differential Privacy for Industrial Internet of Things: Opportunities, Applications and Challenges

Privacy, Biological Relatives, and at-Home DNA Testing

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