{"id":221238,"date":"2024-02-15T17:09:15","date_gmt":"2024-02-15T17:09:15","guid":{"rendered":"https:\/\/bioengineer.org\/shuffling-the-deck-for-privacy\/"},"modified":"2024-02-15T17:09:15","modified_gmt":"2024-02-15T17:09:15","slug":"shuffling-the-deck-for-privacy","status":"publish","type":"post","link":"https:\/\/bioengineer.org\/shuffling-the-deck-for-privacy\/","title":{"rendered":"Shuffling the deck for privacy"},"content":{"rendered":"

By integrating an ensemble of privacy-preserving algorithms, a KAUST research team has developed a machine-learning approach that addresses a significant challenge in medical research: How to use the power of artificial intelligence (AI) to accelerate discovery from genomic data while protecting the privacy of individuals.[1]<\/sup><\/p>\n

\"Shuffling<\/p>\n

Credit: \u00a9 2024 KAUST; Heno Hwang<\/p>\n

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By integrating an ensemble of privacy-preserving algorithms, a KAUST research team has developed a machine-learning approach that addresses a significant challenge in medical research: How to use the power of artificial intelligence (AI) to accelerate discovery from genomic data while protecting the privacy of individuals.[1]<\/sup><\/p>\n

\u201cOmics data usually contains a lot of private information, such as gene expression and cell composition, which could often be related to a person\u2019s disease or health status,\u201d says KAUST\u2019s Xin Gao. \u201cAI models trained on this data \u2013 particularly deep learning models \u2013 have the potential to retain private details about individuals. Our primary focus is finding an improved balance between preserving privacy and optimizing model performance.\u201d<\/p>\n

The traditional approach to preserving privacy is to encrypt the data. However, this requires the data to be decrypted for training, which introduces a heavy computational overhead. The trained model also still retains private information and so can only be used in secure environments.<\/p>\n

Another way to preserve privacy is to break the data into smaller packets and train the model separately on each packet using a team of local training algorithms, an approach known as local training or federated learning. However, on its own, this approach still has the potential to leak private information into the trained model. A method called differential privacy can be used to break up the data in a way that guarantees privacy, but this results in a \u201cnoisy\u201d model that limits its utility for precise gene-based research.<\/p>\n

\u201cUsing the differential privacy framework, adding a shuffler can achieve better model performance while keeping the same level of privacy protection; but the previous approach of using a centralized third-party shuffler that introduces a critical security flaw in that the shuffler could be dishonest,\u201d says Juexiao Zhou, lead author of the paper and a Ph.D. student in Gao\u2019s group. \u201cThe key advance of our approach is the integration of a decentralized shuffling algorithm.\u201d He explains that the shuffler not only resolves this trust issue but achieves a better trade-off between privacy preservation and model capability, while ensuring perfect privacy protection.<\/p>\n

The team demonstrated their privacy-preserving machine-learning approach (called PPML-Omics) by training three representative deep-learning models on three challenging multi-omics tasks. Not only did PPML-Omics produce optimized models with greater efficiency than other approaches, it also proved to be robust against state-of-the-art cyberattacks.<\/p>\n

\u201cIt is important to be aware that proficiently trained deep-learning models possess the ability to retain significant amounts of private information from the training data, such as patients\u2019 characteristic genes,\u201d says Gao. \u201cAs deep learning is being increasingly applied to analyze biological and biomedical data, the importance of privacy protection is greater than ever.\u201d<\/p>\n

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Journal<\/h4>\n

Science Advances<\/p>\n<\/p><\/div>\n

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DOI<\/h4>\n

10.1126\/sciadv.adh8601 <\/i><\/p>\n<\/p><\/div>\n

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Article Title<\/h4>\n

PPML-Omics: a Privacy-Preserving federated Machine Learning method protects patients’ privacy in omic data<\/p>\n<\/p><\/div>\n

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Article Publication Date<\/h4>\n

31-Jan-2024<\/p>\n<\/p><\/div><\/div><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"

By integrating an ensemble of privacy-preserving algorithms, a KAUST research team has developed a machine-learning approach that addresses a significant challenge in medical research: How to use the power of artificial intelligence (AI) to accelerate discovery from genomic data while protecting the privacy of individuals.[1] Credit: \u00a9 2024 KAUST; Heno Hwang By integrating an ensemble […]<\/p>\n","protected":false},"author":8,"featured_media":221239,"comment_status":"","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"jnews-multi-image_gallery":[],"jnews_single_post":[],"jnews_primary_category":[],"jnews_override_bookmark_settings":[],"jnews_social_meta":[],"jnews_review":[],"enable_review":"","type":"","name":"","summary":"","brand":"","sku":"","good":[],"bad":[],"score_override":"","override_value":"","rating":[],"price":[],"jnews_override_counter":[],"footnotes":""},"categories":[185],"tags":[],"class_list":["post-221238","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-science-news"],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/bioengineer.org\/wp-content\/uploads\/2024\/02\/Shuffling-the-deck-for-privacy.jpeg?fit=700%2C394&ssl=1","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/bioengineer.org\/wp-json\/wp\/v2\/posts\/221238","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/bioengineer.org\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/bioengineer.org\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/bioengineer.org\/wp-json\/wp\/v2\/users\/8"}],"replies":[{"embeddable":true,"href":"https:\/\/bioengineer.org\/wp-json\/wp\/v2\/comments?post=221238"}],"version-history":[{"count":0,"href":"https:\/\/bioengineer.org\/wp-json\/wp\/v2\/posts\/221238\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/bioengineer.org\/wp-json\/wp\/v2\/media\/221239"}],"wp:attachment":[{"href":"https:\/\/bioengineer.org\/wp-json\/wp\/v2\/media?parent=221238"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/bioengineer.org\/wp-json\/wp\/v2\/categories?post=221238"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/bioengineer.org\/wp-json\/wp\/v2\/tags?post=221238"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}} BIOENGINEER.ORG

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