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Future Internet
Special Issues
Privacy in Smart Health
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Privacy in Smart Health

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Cybersecurity".

Deadline for manuscript submissions: closed (20 December 2022) | Viewed by 6040

Special Issue Editor

Dr. Rattikorn Hewett

E-Mail Website
Guest Editor
Department of Computer Science, Texas Tech University, Lubbock, TX 79409, USA
Interests: intelligent data understanding; network research and security, software quality assurance

Special Issue Information

Dear Colleagues,

Smart technology plays an important role in our everyday lives. It allows us to communicate, conduct business, and perform tasks remotely. Smart health is no exception, as it continues to make profound impacts on telemedicine, telehealth, and well-being.  Telemedicine provides clinical services where patients can be diagnosed and treated at a distance, whereas telehealth offers healthcare services remotely. Smart health devices are used to monitor patient's vital signs (e.g., glucose, heart rate, and blood pressure). By enabling self-monitoring and data access to users and healthcare professions, smart health can increase healthy behaviors, timely treatments, reduce hospital visits/re-admissions, and save lives. 

Despite many great benefits, smart health comes with new risks of privacy breaches and information leakages during data sharing and dissemination in which the user may lose control of their personal data without knowing, especially when a third party is involved.   Furthermore, when coupled with additional data, one can infer the re-identification of individuals and their sensitive information.

This Special Issue welcomes research relevant to all areas of privacy in smart health. Topics include but are not limited to the following:

  • Privacy-preserving techniques;
  • De-anonymization;
  • Anonymity and privacy measures;
  • Privacy management of healthcare and clinical services;
  • AI-based social inference and automated integrity and privacy.

Dr. Rattikorn Hewett
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

18 pages, 4268 KiB  
Article
Towards Virtuous Cloud Data Storage Using Access Policy Hiding in Ciphertext Policy Attribute-Based Encryption
by Siti Dhalila Mohd Satar, Masnida Hussin, Zurina Mohd Hanapi and Mohamad Afendee Mohamed
Future Internet 2021, 13(11), 279; https://doi.org/10.3390/fi13110279 - 30 Oct 2021
Cited by 7 | Viewed by 2510
Abstract
Managing and controlling access to the tremendous data in Cloud storage is very challenging. Due to various entities engaged in the Cloud environment, there is a high possibility of data tampering. Cloud encryption is being employed to control data access while securing Cloud [...] Read more.
Managing and controlling access to the tremendous data in Cloud storage is very challenging. Due to various entities engaged in the Cloud environment, there is a high possibility of data tampering. Cloud encryption is being employed to control data access while securing Cloud data. The encrypted data are sent to Cloud storage with an access policy defined by the data owner. Only authorized users can decrypt the encrypted data. However, the access policy of the encrypted data is in readable form, which results in privacy leakage. To address this issue, we proposed a reinforcement hiding in access policy over Cloud storage by enhancing the Ciphertext Policy Attribute-based Encryption (CP-ABE) algorithm. Besides the encryption process, the reinforced CP-ABE used logical connective operations to hide the attribute value of data in the access policy. These attributes were converted into scrambled data along with a ciphertext form that provides a better unreadability feature. It means that a two-level concealed tactic is employed to secure data from any unauthorized access during a data transaction. Experimental results revealed that our reinforced CP-ABE had a low computational overhead and consumed low storage costs. Furthermore, a case study on security analysis shows that our approach is secure against a passive attack such as traffic analysis. Full article
(This article belongs to the Special Issue Privacy in Smart Health)
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20 pages, 2172 KiB  
Article
Analytics on Anonymity for Privacy Retention in Smart Health Data
by Sevgi Arca and Rattikorn Hewett
Future Internet 2021, 13(11), 274; https://doi.org/10.3390/fi13110274 - 28 Oct 2021
Cited by 5 | Viewed by 2679
Abstract
Advancements in smart technology, wearable and mobile devices, and Internet of Things, have made smart health an integral part of modern living to better individual healthcare and well-being. By enhancing self-monitoring, data collection and sharing among users and service providers, smart health can [...] Read more.
Advancements in smart technology, wearable and mobile devices, and Internet of Things, have made smart health an integral part of modern living to better individual healthcare and well-being. By enhancing self-monitoring, data collection and sharing among users and service providers, smart health can increase healthy lifestyles, timely treatments, and save lives. However, as health data become larger and more accessible to multiple parties, they become vulnerable to privacy attacks. One way to safeguard privacy is to increase users’ anonymity as anonymity increases indistinguishability making it harder for re-identification. Still the challenge is not only to preserve data privacy but also to ensure that the shared data are sufficiently informative to be useful. Our research studies health data analytics focusing on anonymity for privacy protection. This paper presents a multi-faceted analytical approach to (1) identifying attributes susceptible to information leakages by using entropy-based measure to analyze information loss, (2) anonymizing the data by generalization using attribute hierarchies, and (3) balancing between anonymity and informativeness by our anonymization technique that produces anonymized data satisfying a given anonymity requirement while optimizing data retention. Our anonymization technique is an automated Artificial Intelligent search based on two simple heuristics. The paper describes and illustrates the detailed approach and analytics including pre and post anonymization analytics. Experiments on published data are performed on the anonymization technique. Results, compared with other similar techniques, show that our anonymization technique gives the most effective data sharing solution, with respect to computational cost and balancing between anonymity and data retention. Full article
(This article belongs to the Special Issue Privacy in Smart Health)
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