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Reliable medical recommendation systems with patient privacy

Authors:

Marina Blanton,

Nitesh V. ChawlaAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 4, Issue 4

Article No.: 67, Pages 1 - 31

https://doi.org/10.1145/2508037.2508048

Published: 08 October 2013 Publication History

Abstract

One of the concerns patients have when confronted with a medical condition is which physician to trust. Any recommendation system that seeks to answer this question must ensure that any sensitive medical information collected by the system is properly secured. In this article, we codify these privacy concerns in a privacy-friendly framework and present two architectures that realize it: the Secure Processing Architecture (SPA) and the Anonymous Contributions Architecture (ACA). In SPA, patients submit their ratings in a protected form without revealing any information about their data and the computation of recommendations proceeds over the protected data using secure multiparty computation techniques. In ACA, patients submit their ratings in the clear, but no link between a submission and patient data can be made. We discuss various aspects of both architectures, including techniques for ensuring reliability of computed recommendations and system performance, and provide their comparison.

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 4, Issue 4

Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers

September 2013

452 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2508037

Issue’s Table of Contents

Copyright © 2013 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 October 2013

Accepted: 01 August 2012

Revised: 01 May 2012

Received: 01 December 2011

Published in TIST Volume 4, Issue 4

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Division of Behavioral and Cognitive Sciences
Air Force Office of Scientific Research
Richard and Peggy Notebaert Premier Fellowship

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Cited By

Vetrivel SArun VSowmiya KSabareeshwari V(2024)Personalized Healthcare in the Era of E-Business 5.0Smart Technologies and Innovations in E-Business10.4018/978-1-6684-7840-0.ch010(186-203)Online publication date: 19-Jul-2024
https://doi.org/10.4018/978-1-6684-7840-0.ch010
Pham DSeo YPhan H(2024)Recommendation Systems for the Healthcare Domain: A Comprehensive Survey of Evaluation DatasetsVietnam Journal of Computer Science10.1142/S219688882450016711:04(487-529)Online publication date: 31-Aug-2024
https://doi.org/10.1142/S2196888824500167
Pan QZhang J(2024)DiagNCF: Diagnosis Neural Collaborative Filtering for Accurate Medical RecommendationAdvanced Intelligent Computing in Bioinformatics10.1007/978-981-97-5692-6_10(108-118)Online publication date: 5-Aug-2024
https://dl.acm.org/doi/10.1007/978-981-97-5692-6_10
Yadav A(2024)Text Mining for Recommendation Systems/Expert Systems in Health DomainText Mining Approaches for Biomedical Data10.1007/978-981-97-3962-2_19(403-409)Online publication date: 4-Sep-2024
https://doi.org/10.1007/978-981-97-3962-2_19
Huarng KYu THuang D(2023)Artificial Intelligence in HealthcarePhilosophy of Artificial Intelligence and Its Place in Society10.4018/978-1-6684-9591-9.ch003(43-55)Online publication date: 30-Jun-2023
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Lyu YXu QYang ZLiu J(2023)Prediction of patient choice tendency in medical decision-making based on machine learning algorithmFrontiers in Public Health10.3389/fpubh.2023.108735811Online publication date: 24-Feb-2023
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Howell PAryal AWu C(2023)Web-Based Patient Recommender Systems for Preventive Care: Protocol for Empirical Research PropositionsJMIR Research Protocols10.2196/4331612(e43316)Online publication date: 30-Mar-2023
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Nguyen MNguyen DTrinh QLe B(2023)ALGNet: Attention Light Graph Memory Network for Medical Recommendation SystemProceedings of the 12th International Symposium on Information and Communication Technology10.1145/3628797.3628983(570-577)Online publication date: 7-Dec-2023
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Müllner PLex ESchedl MKowald D(2023)ReuseKNN: Neighborhood Reuse for Differentially Private KNN-Based RecommendationsACM Transactions on Intelligent Systems and Technology10.1145/360848114:5(1-29)Online publication date: 11-Aug-2023
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Cheng YChen HXu LChen KWang XHuang Z(2023)Exploring The Role of Big Data Algorithm Recommendation in Smart Cities- Taking Book recommendation As an Example2023 International Conference on IT Innovation and Knowledge Discovery (ITIKD)10.1109/ITIKD56332.2023.10099656(1-5)Online publication date: 8-Mar-2023
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