A privacy-aware cloud-assisted healthcare monitoring system via compressive sensing

Wang, Cong; Zhang, Bingsheng; Ren, Kui; Roveda, Janet; Chen, Chang Wen; Xu, Zhen

doi:10.1109/infocom.2014.6848155

Cited by 49 publications

(37 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fiore and Gennaro [64] proposed an extending scheme for polynomial evaluation and matrix-vector multiplication with an improved PRF algorithm. Wang et al [65] employed a random transformation based approach where all the matrices and vectors for sampling and secret transformation are generated by a PRF with random seeds. The sharing of the private matrices and vectors is simplified to shares of the random seeds.…”

Section: B Pseudorandom Functionsmentioning

confidence: 99%

A Comprehensive Survey on Secure Outsourced Computation and Its Applications

et al. 2019

View full text Add to dashboard Cite

With the ever-increasing requirement of storage and computation resources, it is unrealistic for local devices (with limited sources) to implement large-scale data processing. Therefore, individuals or corporations incline to outsource their computation requirements to the cloud. However, data outsourcing brings security and privacy concerns to users when the cloud servers are not fully trusted. Recently, extensive research works are conducted, aiming at secure outsourcing schemes for diverse computational tasks via different technologies. In this survey, we provide a technical review and comparison of existing outsourcing schemes using diverse secure computation methods. Specifically, we begin the survey by describing security threats and requirements of secure outsourcing computation. Meanwhile, we introduce four secure techniques (i.e., secure multi-party computation, pseudorandom functions, software guard extensions, and perturbation approaches) and their related works. Then, we focus on the theories and evolution of homomorphic encryption, as well as the applications of the basic operations and application-specific tasks. Finally, we discuss the security and performance of existing works and give future directions in this field. INDEX TERMS Secure outsourced computing, privacy preserving, homomorphic encryption, secure outsourced machine learning, data processing.

show abstract

Section: B Pseudorandom Functionsmentioning

confidence: 99%

A Comprehensive Survey on Secure Outsourced Computation and Its Applications

et al. 2019

View full text Add to dashboard Cite

show abstract

“…In addition, WSNs have witnessed some applications where CS has been used as both acquisition and encryption scheme. Wang et al proposed a two-level CS-based security platform to transmit the data from the sensors to the cloud [163]. The first level of encryption is performed by acquiring data following the CS concept.…”

Section: Data Securitymentioning

confidence: 99%

Compressive Sensing-Based IoT Applications: A Review

Djelouat

Amira

Bensaali

2018

JSAN

View full text Add to dashboard Cite

The Internet of Things (IoT) holds great promises to provide an edge cutting technology that enables numerous innovative services related to healthcare, manufacturing, smart cities and various human daily activities. In a typical IoT scenario, a large number of self-powered smart devices collect real-world data and communicate with each other and with the cloud through a wireless link in order to exchange information and to provide specific services. However, the high energy consumption associated with the wireless transmission limits the performance of these IoT self-powered devices in terms of computation abilities and battery lifetime. Thus, to optimize data transmission, different approaches have to be explored such as cooperative transmission, multi-hop network architectures and sophisticated compression techniques. For the latter, compressive sensing (CS) is a very attractive paradigm to be incorporated in the design of IoT platforms. CS is a novel signal acquisition and compression theory that exploits the sparsity behavior of most natural signals and IoT architectures to achieve power-efficient, real-time platforms that can grant efficient IoT applications. This paper assesses the extant literature that has aimed to incorporate CS in IoT applications. Moreover, the paper highlights emerging trends and identifies several avenues for future CS-based IoT research.

show abstract

“…Zhang et al [41] evaluated the performance of encompression (encryption+compression) on commercial embedded sensor platforms, showing that with a reasonable compression, encompression could considerably reduce the communication overhead and total energy consumption. Wang et al [42] formulated a privacy-preserving healthcare monitoring system for image data, employing compressive sensing to reduce the energy consumption of sensors by outsourcing the reconstruction to the cloud.…”

Section: B Compressive Sensingmentioning

confidence: 99%

e-SAFE: Secure, Efficient and Forensics-Enabled Access to Implantable Medical Devices

Chi

et al. 2018

2018 IEEE Conference on Communications and Network Security (CNS)

View full text Add to dashboard Cite

To facilitate monitoring and management, modern Implantable Medical Devices (IMDs) are often equipped with wireless capabilities, which raise the risk of malicious access to IMDs. Although schemes are proposed to secure the IMD access, some issues are still open. First, pre-sharing a long-term key between a patient's IMD and a doctor's programmer is vulnerable since once the doctor's programmer is compromised, all of her patients suffer; establishing a temporary key by leveraging proximity gets rid of pre-shared keys, but as the approach lacks real authentication, it can be exploited by nearby adversaries or through man-in-the-middle attacks. Second, while prolonging the lifetime of IMDs is one of the most important design goals, few schemes explore to lower the communication and computation overhead all at once. Finally, how to safely record the commands issued by doctors for the purpose of forensics, which can be the last measure to protect the patients' rights, is commonly omitted in the existing literature. Motivated by these important yet open problems, we propose an innovative scheme e-SAFE, which significantly improves security and safety, reduces the communication overhead and enables IMD-access forensics. We present a novel lightweight compressive sensing based encryption algorithm to encrypt and compress the IMD data simultaneously, reducing the data transmission overhead by over 50% while ensuring high data confidentiality and usability. Furthermore, we provide a suite of protocols regarding device pairing, dual-factor authentication, and accountability-enabled access. The security analysis and performance evaluation show the validity and efficiency of the proposed scheme.

show abstract

A privacy-aware cloud-assisted healthcare monitoring system via compressive sensing

Cited by 49 publications

References 17 publications

A Comprehensive Survey on Secure Outsourced Computation and Its Applications

A Comprehensive Survey on Secure Outsourced Computation and Its Applications

Compressive Sensing-Based IoT Applications: A Review

e-SAFE: Secure, Efficient and Forensics-Enabled Access to Implantable Medical Devices

Contact Info

Product

Resources

About