Privacy Preserving Cloud-assisted mobile health (mHealth) monitoring


Cloud-assisted mobile health (mHealth) monitoring, which applies the prevailing mobile communications and cloud computing technologies to provide feedback decision support, has been considered as a revolutionary approach to improving the quality of healthcare service while lowering the health care cost. Unfortunately, it also poses a serious risk to both clients’privacy and intellectual property of monitoring service providers, which could deter the wide adoption of mHealth technology. This paper is to address this important problem and design a cloud-assisted privacy-preserving mobile health monitoring system to protect the privacy of the involved parties and their data.Moreover, the outsourcing decryption technique and a newly proposed key private proxy re-encryption are adapted to shift the computational complexity of the involved parties to the cloud without compromising clients’ privacy and service providers’intellectual property. Finally, our security and performance analysis demonstrates the effectiveness of our proposed design.


Traditional privacy protection mechanisms by simply removing clients’ personal identity information (such as names or SSN) or by using anonymization technique fails to serve as an effective way in dealing with the privacy of health systems due to the increasing amount and diversity of personally identifiable information.
Traditionally, the privacy issue is tackled with anonymization technique such as k-anonymity or l-diversity.However, it has been indicated that these techniques might be insufficient to prevent re-identification attack


Unfortunately, although cloud-assisted health monitoring could offer a great opportunity to improve the quality of healthcare services and potentially reduce healthcare costs, there is a stumbling block in making this technology reality. Without properly addressing the data management inanmHealth system, clients’ privacy may be severely breached during the collection, storage, diagnosis, communications, and computing.
Another major problem in addressing security and privacy is the computational workload involved with the cryptographic techniques. With the presence of cloud computing facilities, it will be wise to shift intensive computations to cloud servers from resource-constrained mobile devices. However, how to achieve this effect without compromising privacy and security become a great challenge, which should be carefully investigated.


In this paper, we design a cloud-assisted mHealth monitoring system (CAM). We first identify the design problems privacy preservation and then provide our solutions. To ease the understanding, we start with the basic scheme so that we can identify the possible privacy breaches. We then provide an improved scheme by addressing the identified privacy problems. The resulting improved scheme allows the health service provider (the company) to be offline at the setup stage and enables it to deliver its data or programs to the cloud securely.
To reduce clients’ decryption complexity, we incorporate the recently proposed outsourcing decryption technique into the underlying multi-dimensional range queries system to shift clients’ computational complexity to the cloud without revealing any information on either clients’query input or the decrypted decision to the cloud. To relieve the computational complexity on the company’s side, which is proportional to the number of clients, we propose a further improvement, leading to our final scheme. It is based on anew variant of key private proxy re-encryption scheme, in which the company only needs to accomplish encryption once the setup phase while shifting the rest computational tasks to the cloud without compromising privacy, further reducing the computational and communication burden on clients and the cloud


  1. To protect the clients’privacy, we apply the anonymous Boneh-Franklin identity-based encryption (IBE) in medical diagnostic branching programs.
  2. To reduce the decryption complexity due to the use ofIBE, we apply recently proposed decryption outsourcing with privacy protection to shift clients’ pairing computation to the cloud server.
  3. To protect health service providers’ programs, we expand the branching program tree by using the random permutation and randomize the decision thresholds used at the decision branching nodes.

Download: CAM Cloud-Assisted Privacy Preserving Mobile


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