Biblio

Wearable devices are emerging as effective modalities for the collection of individuals’ data. While this data can be leveraged for use in several areas ranging from health-care to crime investigation, storing and securely accessing such information while preserving privacy and detecting any tampering attempts are significant challenges. This paper describes a decentralized system that ensures an individual’s privacy, maintains an immutable log of any data access, and provides decentralized access control management. Our proposed framework uses a custom permissioned blockchain protocol to securely log data transactions from wearable devices in the blockchain ledger. We have implemented a proof-of-concept for our framework, and our preliminary evaluation is summarized to demonstrate our proposed framework’s capabilities. We have also discussed various application scenarios of our privacy-preserving model using blockchain and proof-of-authority. Our research aims to detect data tampering attempts in data sharing scenarios using a thorough transaction log model.

Digital connectivity is fundamental to the health care system to deliver safe and effective care. However, insecure connectivity could be a major threat to patient safety and privacy (e.g., in August 2017, FDA recalled 465,000 pacemakers because of discovering security flaws). Although connecting a patient's pacemaker to the Internet has many advantages for monitoring the patient, this connectivity opens a new door for cyber-attackers to steal the patient data or even control the pacemaker or damage it. Therefore, patients are forced to choose between connectivity and security. This paper presents a framework for secure and private communications between wearable medical devices and patient monitoring systems. The primary objective of this research is twofold, first to identify and analyze the communication vulnerabilities, second, to develop a framework for combating unauthorized access to data through the compromising of computer security. Specifically, hiding targets from cyber-attackers could prevent our system from future cyber-attacks. This is the most effective way to stop cyber-attacks in their first step.

In today's world privacy is paramount in everyone's life. Alongside the growth of IoT (Internet of things), wearable devices are becoming widely popular for real-time user monitoring and wise service support. However, in contrast with the traditional short-range communications, these resource-scanty devices face various vulnerabilities and security threats during the course of interactions. Hence, designing a security solution for these devices while dealing with the limited communication and computation capabilities is a challenging task. In this work, PUF (Physical Unclonable Function) and lightweight cryptographic parameters are used together for performing two-way authentication between wearable devices and smartphone, while the simultaneous verification is performed by providing yoking-proofs to the Cloud Server. At the end, it is shown that the proposed scheme satisfies many security aspects and is flexible as well as lightweight.

Numerous antenna design approaches for wearable applications have been investigated in the literature. As on-body wearable communications become more ingrained in our daily activities, the necessity to investigate the impacts of these networks burgeons as a major requirement. In this study, we investigate the human electromagnetic field (EMF) exposure effect from on-body wearable devices at 2.4 GHz and 60 GHz, and compare the results to illustrate how the technology evolution to higher frequencies from wearable communications can impact our health. Our results suggest the average specific absorption rate (SAR) at 60 GHz can exceed the regulatory guidelines within a certain separation distance between a wearable device and the human skin surface. To the best of authors' knowledge, this is the first work that explicitly compares the human EMF exposure at different operating frequencies for on-body wearable communications, which provides a direct roadmap in design of wearable devices to be deployed in the Internet of Battlefield Things (IoBT).

Sharing personal data with other people or organizations over the web has become a common phenomena of our modern life. This type of sharing is usually managed by access control mechanisms that include access control model and policies. However, these models are designed from the organizational perspective and do not provide sufficient flexibility and control to the individuals. Therefore, individuals often cannot control sharing of their personal data based on their personal context. In addition, the existing context-aware access control models usually check contextual condition once at the beginning of the access and do not evaluate the context during an on-going access. Moreover, individuals do not have control to define how often they want to evaluate the context condition for an ongoing access. Wearable devices such as Fitbit and Apple Smart Watch have recently become increasingly popular. This has made it possible to gather an individual's real-time contextual information (e.g., location, blood-pressure etc.) which can be used to enforce continuous authorization to the individual's data resources. In this paper, we introduce a novel data sharing policy model for continuous authorization in subject-driven data sharing. A software prototype has been implemented employing a wearable device to demonstrate continuous authorization. Our continuous authorization framework provides more control to the individuals by enabling revocation of on-going access to shared data if the specified context condition becomes invalid.

Wearables are now ubiquitous items equipped with a multitude of sensors such as GPS, accelerometer, or Bluetooth. The raw data from this sensors are typically used in a health context. However, we can also use it for security purposes. In this paper, we present a solution that aims at using data from the sensors of a wearable device to identify the password a user is typing on a keyboard by using machine learning algorithms. Hence, the purpose is to determine whether a malicious third party application could extract sensitive data through the raw data that it has access to.

Nowadays, IoT has crossed all borders and become ubiquitous in everyday life. This emerging technology has a huge success in closing the gap between the digital and the real world. However, security and privacy become huge concerns especially in the medical field which prevent the healthcare industry from adopting it despite its benefits and potentials. This paper focuses on identifying potential security threats to the IoMT and presents the security mechanisms to remove any possible impediment from immune information security of IoMT. A summarized framework of the layered-security model is proposed followed by a specific assessment review of each layer.

The data transmitted from the wearable device commonly includes sensitive data. So, application service using the data collected from the unauthorized wearable devices can cause serious problems. Also, it is important to authenticate any wearable device and then, protect the transmitted data between the wearable devices and the application server. In this paper, we propose an authentication protocol, which is designed by using the Transport Layer Security (TLS) handshake protocol combined with a mobile authentication proxy. By using the proposed authentication protocol, we can authenticate the wearable device. And we can secure data transmission since session key is shared between the wearable device and the application server. In addition, the proposed authentication protocol is secure even when the mobile authentication proxy is unreliable.

Mobile payment has drawn considerable attention due to its convenience of paying via personal mobile devices at anytime and anywhere, and passcodes (i.e., PINs) are the first choice of most consumers to authorize the payment. This work demonstrates a serious security breach and aims to raise the awareness of the public that the passcodes for authorizing transactions in mobile payments can be leaked by exploiting the embedded sensors in wearable devices (e.g., smartwatches). We present a passcode inference system, which examines to what extent the user's PIN during mobile payment could be revealed from a single wrist-worn wearable device under different input scenarios involving either two hands or a single hand. Extensive experiments with 15 volunteers demonstrate that an adversary is able to recover a user's PIN with high success rate within 5 tries under various input scenarios.

Wearable medical devices are playing more and more important roles in healthcare. Unlike the wired connection, the wireless connection between wearable devices and the remote servers are exceptionally vulnerable to malicious attacks, and poses threats to the safety and privacy of the patient health data. Therefore, wearable medical devices require the implementation of reliable measures to secure the wireless network communication. However, those devices usually have limited computational power that is not comparable with the desktop computer and thus, it is difficult to adopt the full-fledged security algorithm in software. In this study, we have developed an efficient authentication and encryption protocol for internetconnected wearable devices using the recognized standards of AES and SHA that can provide two-way authentication between wearable device and remote server and protection of patient privacy against various network threats. We have tested the feasibility of this protocol on the TI CC3200 Launchpad, an evaluation board of the CC3200, which is a Wi-Fi capable microcontroller designed for wearable devices and includes a hardware accelerated cryptography module for the implementation of the encryption algorithm. The microcontroller serves as the wearable device client and a Linux computer serves as the server. The embedded client software was written in ANSI C and the server software was written in Python.

Wearable devices, which are small electronic devices worn on a human body, are equipped with low level of processing and storage capacities and offer some types of integrated functionalities. Recently, wearable device is becoming increasingly popular, various kinds of wearable device are launched in the market; however, wearable devices require a powerful local-hub, most are smartphone, to replenish processing and storage capacities for advanced functionalities. Sometime it may be inconvenient to carry the local-hub (smartphone); thus, many wearable devices are equipped with Wi-Fi interface, enabling them to exchange data with local-hub though the Internet when the local-hub is not nearby. However, this results in long response time and restricted functionalities. In this paper, we present a virtual local-hub solution, which utilizes network equipment nearby (e.g., Wi-Fi APs) as the local-hub. Since migrating all applications serving the wearable devices respectively takes too much networking and storage resources, the proposed solution deploys function modules to multiple network nodes and enables remote function module sharing among different users and applications. To reduce the impact of the solution on the network bandwidth, we propose a heuristic algorithm for function module allocation with the objective of minimizing total bandwidth consumption. We conduct series of experiments, and the results show that the proposed solution can reduce the bandwidth consumption by up to half and still serve all requests given a large number of service requests.

With the increasing popularity of wearable devices, information becomes much easily available. However, personal information sharing still poses great challenges because of privacy issues. We propose an idea of Visual Human Signature (VHS) which can represent each person uniquely even captured in different views/poses by wearable cameras. We evaluate the performance of multiple effective modalities for recognizing an identity, including facial appearance, visual patches, facial attributes and clothing attributes. We propose to emphasize significant dimensions and do weighted voting fusion for incorporating the modalities to improve the VHS recognition. By jointly considering multiple modalities, the VHS recognition rate can reach by 51% in frontal images and 48% in the more challenging environment and our approach can surpass the baseline with average fusion by 25% and 16%. We also introduce Multiview Celebrity Identity Dataset (MCID), a new dataset containing hundreds of identities with different view and clothing for comprehensive evaluation.