research-article

Privacy Preserving Disease Treatment & Complication Prediction System (PDTCPS)

Authors:

Mooi Choo Chuah,

Yingying ChenAuthors Info & Claims

ASIA CCS '16: Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security

Pages 841 - 852

https://doi.org/10.1145/2897845.2897893

Published: 30 May 2016 Publication History

Abstract

Affordable cloud computing technologies allow users to efficiently store, and manage their Personal Health Records (PHRs) and share with their caregivers or physicians. This in turn improves the quality of healthcare services, and lower health care cost. However, serious security and privacy concerns emerge because people upload their personal information and PHRs to the public cloud. Data encryption provides privacy protection of medical information but it is challenging to utilize encrypted data. In this paper, we present a privacy-preserving disease treatment, complication prediction scheme (PDTCPS), which allows authorized users to conduct searches for disease diagnosis, personalized treatments, and prediction of potential complications. $PDTCPS$ uses a tree-based structure to boost search efficiency, a wildcard approach to support fuzzy keyword search, and a Bloom-filter to improve search accuracy and storage efficiency. In addition, our design also allows health care providers and the public cloud to collectively generate aggregated training models for disease diagnosis, personalized treatments and complications prediction. Moreover, our design provides query unlinkability and hides both search & access patterns. Finally, our evaluation results using two UCI datasets show that our scheme is more efficient and accurate than two existing schemes.

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Cited By

Zhang MZhang YShen G(2022)PPDDS: A Privacy-Preserving Disease Diagnosis Scheme Based on the Secure Mahalanobis Distance Evaluation ModelIEEE Systems Journal10.1109/JSYST.2021.309341516:3(4552-4562)Online publication date: Sep-2022
https://doi.org/10.1109/JSYST.2021.3093415
Xue QChuah MBonato PWang H(2017)Incentivising high quality crowdsourcing clinical data for disease predictionProceedings of the Second IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies10.5555/3204094.3204121(185-194)Online publication date: 17-Jul-2017
https://dl.acm.org/doi/10.5555/3204094.3204121
Xue QChuah M(2017)Incentivising High Quality Crowdsourcing Clinical Data for Disease Prediction2017 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)10.1109/CHASE.2017.77(185-194)Online publication date: Jul-2017
https://doi.org/10.1109/CHASE.2017.77

Index Terms

Privacy Preserving Disease Treatment & Complication Prediction System (PDTCPS)
1. Information systems
  1. Data management systems
    1. Data structures
      1. Data layout
        Data encryption
  2. Information retrieval
    1. Information retrieval query processing
2. Security and privacy
  1. Cryptography
  2. Security services
    1. Privacy-preserving protocols

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Published In

cover image ACM Conferences

ASIA CCS '16: Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security

May 2016

958 pages

ISBN:9781450342339

DOI:10.1145/2897845

General Chair:
Xiaofeng Chen
Xidian University, China
,
Program Chairs:
XiaoFeng Wang
Indiana University at Bloomington, USA
,
Xinyi Huang
Fujian Normal University, China

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSAC: ACM Special Interest Group on Security, Audit, and Control

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 May 2016

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US National Science Foundation

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ASIA CCS '16

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SIGSAC

ASIA CCS '16: ACM Asia Conference on Computer and Communications Security

May 30 - June 3, 2016

Xi'an, China

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ASIA CCS '16 Paper Acceptance Rate 73 of 350 submissions, 21%;

Overall Acceptance Rate 418 of 2,322 submissions, 18%

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Cited By

Zhang MZhang YShen G(2022)PPDDS: A Privacy-Preserving Disease Diagnosis Scheme Based on the Secure Mahalanobis Distance Evaluation ModelIEEE Systems Journal10.1109/JSYST.2021.309341516:3(4552-4562)Online publication date: Sep-2022
https://doi.org/10.1109/JSYST.2021.3093415
Xue QChuah MBonato PWang H(2017)Incentivising high quality crowdsourcing clinical data for disease predictionProceedings of the Second IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies10.5555/3204094.3204121(185-194)Online publication date: 17-Jul-2017
https://dl.acm.org/doi/10.5555/3204094.3204121
Xue QChuah M(2017)Incentivising High Quality Crowdsourcing Clinical Data for Disease Prediction2017 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)10.1109/CHASE.2017.77(185-194)Online publication date: Jul-2017
https://doi.org/10.1109/CHASE.2017.77

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