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.

We analyze partner health apps compatible with the Fitbit fitness tracker, and record what third parties they are talking to. We focus on the ten partner Android applications that have more than 50,000 downloads and are fitness-related. Our results show that most of the them contact “unexpected” third parties. Such third parties include social networks; analytics and advertisement services; weather APIs. We also investigate what information is shared by the partner apps with these unexpected entities. Our findings suggest that in many cases personal information of users might be shared, including the phone model; location and SIM carrier; email and connection history.

Purpose: Smart apps and wearables devices are an increasingly used way in healthcare to monitor a range of functions associated with certain health conditions. Even if in the present there are some devices and applications developed, there is no sufficient evidence of the use of such wearables devices in the detection of some disorders such as depression. Thus, through this paper, we want to address this need and present a quantitative research to determine the user requirements for developing a smart device that can detect depression. Material and Methods: To determine the user requirements for developing a system to detect depression we developed a questionnaire which was applied to 205 participants. Results and conclusions: Such a system addressed to detect depression is of interest among the respondents. The most essential parameters to be monitored refer to sleep quality, level of stress, circadian rhythm, and heart rate. Also, the developed system should prioritize reliability, privacy, security, and ease of use.

The use of wearable devices (e.g., smartwatches) in two factor authentication (2FA) is fast emerging, as wearables promise better usability compared to smartphones. Still, the current deployments of wearable 2FA have significant usability and security issues. Specifically, one-time PIN-based wearable 2FA (PIN-2FA) requires noticeable user effort to open the app and copy random PINs from the wearable to the login terminal's (desktop/laptop) browser. An alternative approach, based on one-tap approvals via push notifications (Tap-2FA), relies upon user decision making to thwart attacks and is prone to skip-through. Both approaches are also vulnerable to traditional phishing attacks. To address this security-usability tension, we introduce a fundamentally different design of wearable 2FA, called SG-2FA, involving wrist-movement “seamless gestures” captured near transparently by the second factor wearable device while the user types a very short special sequence on the browser during the login process. The typing of the special sequence creates a wrist gesture that when identified correctly uniquely associates the login attempt with the device's owner. The special sequence can be fixed (e.g., “\$@\$@\$\$”), does not need to be a secret, and does not need to be memorized (could be simply displayed on the browser). This design improves usability over PIN-2FA since only this short sequence has to be typed as part of the login process (no interaction with or diversion of attention to the wearable and copying of random PINs is needed). It also greatly improves security compared to Tap-2FA since the attacker can not succeed in login unless the user's wrist is undergoing the exact same gesture at the exact same time. Moreover, the approach is phishing-resistant and privacy-preserving (unlike behavioral biometrics). Our results show that SG-2FA incurs only minimal errors in both benign and adversarial settings based on appropriate parameterizations.

An important demand of a wearable health observance system is to soundly exchange the Employees' health data and preventing improper use of black devices. In this project we tend to measure planning wearable sensors device sight abnormal and/or unforeseen things by observance physiological parameters alongside different symptoms. Therefore, necessary facilitate is provided in times of urgent would like. To minimize the health hazards and improving the well-being of employees is to be a major critical role in an organization. As per the report by the Indian Labour Organization, the organization spends an average of 3.94% for GDP on employee treatment. The same study revealed that almost 2.78% million deaths occurs every year and 3.74% million occur non-fatal injuries every year at work. So, the organizations are making towards mitigating the facilities to decimating various IoT technologies and the IoT technology are embedded with modern smart systems, it is easy to monitor every employee in an organization, and also it collects and gather the data and send any critical information by the employees.

Recent years have seen an increase in medical big data, which can be attributed to a paradigm shift experienced in medical data sharing induced by the growth of medical technology and the Internet of Things. The evidence of this potential has been proved during the recent covid-19 pandemic, which was characterised by the use of medical wearable devices to help with the medical data exchange between the healthcare providers and patients in a bid to contain the pandemic. However, the use of these technologies has also raised questions and concerns about security and privacy risks. To assist in resolving this issue, this paper proposes a blockchain-based access control framework for managing access to users’ medical data. This is facilitated by using a smart contract on the blockchain, which allows for delegated access control and secure user authentication. This solution leverages blockchain technology’s inherent autonomy and immutability to solve the existing access control challenges. We have presented the solution in the form of a medical wearable sensor prototype and a mobile app that uses the Ethereum blockchain in a real data sharing control scenario. Based on the empirical results, the proposed solution has proven effective. It has the potential to facilitate reliable data exchange while also protecting sensitive health information against potential threats. When subjected to security analysis and evaluation, the system exhibits performance improvements in data privacy levels, high security and lightweight access control design compared to the current centralised access control models.

Privacy and security are the key challenges of wearable IoTs. Smart wearables are becoming popular choice of people because of their indispensable application in the field of clinical medication and medical care, wellbeing the executives, working environments, training, and logical examination. Currently, IoT is facing several challenges, such as- user unawareness, lack of efficient security protocols, vulnerable wireless communication and device management, and improper device management. The paper investigates a efficient audit of safety and protection issues involved in wearable IoT devices with the following structure, as- (i) Background of IoT systems and applications (ii) Security and privacy issues in IoT (iii) Popular wearable IoTs in demand (iv) Highlight the existing IoT security and privacy solutions, and (v) Approaches to secure the futuristic IoT based environment. Finally, this study summarized with security vulnerabilities in IoT, Countermeasures and existing security and privacy solutions, and futuristic smart wearables.

In this paper, we propose a functional model for the implementation of devices that use the Internet of Things (IoT). In recent years, the number of devices connected to the internet per person has increased from 0.08 in 2003 to a total of 6.58 in 2020, suggesting an increase of 8,225% in 7 years. The proposal includes a functional IoT model of a cybersecurity architecture by including components to ensure compliance with the proposed controls within a cybersecurity framework to detect cyber threats in IoT-based wearable devices. The proposal focuses on reducing the number of vulnerabilities present in IoT devices since, on average, 57% of these devices are vulnerable to attacks. The model has a 3-layer structure: business, applications, and technology, where components such as policies, services and nodes are described accordingly. The validation was done through a simulated environment of a system for the control and monitoring of pregnant women using wearable devices. The results show reductions of the probability index and the impact of risks by 14.95% and 6.81% respectively.

Aim and scope of the study From 2017 to 2020, we conducted a research through design to address a number of identified obstacles to adoption of wearable computing. One obstacle was a perceived failure to design wearables for emotional engagement [1] [2] [3]. To address this, we began the inspiration phase with a participatory design process with an open-ended brief, instead of the typical approach of starting with a design exemplar. In this way, we elicited concepts from the participants to discover what kinds of everyday wearables they desired [4], rather than their preferences for some particular device type like an activity monitor [5]. The obstacles interrelate, and the outcome of our investigations against the obstacle of poor emotional engagement, give cause to reflect on another of the obstacles: privacy. This paper will reflect on the privacy issues evoked by our experience.

Recently, the frequent security incidents of the Internet of things make the wearable IOT health monitoring systems (WIHMS) face serious security threats. Aiming at the security requirements of WIHMS identity authentication, Q. Jiang proposed a lightweight device mutual identity authentication solution in 2019. The scheme has good security performance. However, we find that in Jiang’s scheme, in the authentication phase, the server CS needs at least 3 queries and 1 update of the database operation, which affects the overall performance of the system. For this reason, we propose a new device mutual authentication and key agreement protocol. In our protocol, the authentication server only needs to query the server database twice.

Technology is advancing rapidly and with this advancement, it has become apparent that it is nearly impossible to not leave a digital trace when committing a crime. As evidenced by multiple cases handled by law enforcement, Fitbit data has proved to be useful when determining the validity of alibis and in piecing together the timeline of a crime scene. In our paper, experiments testing the accuracy and reliability of GPS-tracked activities logged by the Fitbit Alta tracker and Ionic smartwatch are conducted. Potential indicators of manipulated or altered GPS-tracked activities are identified to help guide digital forensic investigators when handling such Fitbit data as evidence.

This paper presents a proximity coupled wideband wearable antenna operating between 4.71 GHz and 5.81 GHz with 5.2 GHz as centre frequency for biomedical telemetry applications in ISM band (IEEE 802.11 Standard). Two layers of different flexible substrate materials, ethylene-vinyl acetate and felt make the design mechanically stable. Bandwidth improvement is achieved by introducing two slots on elliptical ground plane. Highest gain of 3.72 dB and front to back ratio (FBR) of 6.55 is obtained in the given frequency band. The dimensions of antenna have been optimized to have desired bandwidth of 1100 MHz (\$\textbackslashtextbackslashsimeq\$21%). The specific absorption rate (SAR) value is 1.12 \$W/Kg\$ for 1 g of human body tissue. Both simulated and measured results are presented for the structure.

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.

The developments made in IoT applications have made wearable devices a popular choice for collecting user data to monitor this information and provide intelligent service support. Since wearable devices are continuously collecting and transporting a user's sensitive data over the network, there exist increased security challenges. Moreover, wearable devices lack the computation capabilities in comparison to traditional short-range communication devices. In this paper, authors propounded a Yoking Proof based remote Authentication scheme for Cloud-aided Wearable devices (YPACW) which takes PUF and cryptographic functions and joins them to achieve mutual authentication between the wearable devices and smartphone via a cloud server, by performing the simultaneous verification of these devices, using the established yoking-proofs. Relative to Liu et al.'s scheme, YPACW provides better results with the reduction of communication and processing cost significantly.

The Internet of Things (IoT) is a system paradigm that recently introduced, which includes different smart devices and applications, especially, in smart cities, e.g.; manufacturing, homes, and offices. To improve their awareness capabilities, it is attractive to add more sensors to their framework. In this paper, we propose adding a new sensor as a wearable sensor connected wirelessly with a neural network located on the base station (WSNB). WSNB enables the added sensor to refine their labels through active learning. The new sensors achieve an average accuracy of 93.81%, which is 4.5% higher than the existing method, removing human support and increasing the life cycle for the sensors by using neural network approach in the base station.

Wearable technology, such as Fitbit devices, log a user's daily activities, heart rate, calories burned, step count, and sleep activity. This information is valuable to digital forensic investigators as it may serve as evidence to a crime, to either support a suspect's innocence or guilt. It is important for an investigator to find and analyze every piece of data for accuracy and integrity; however, there is no standard for conducting a forensic investigation for wearable technology. In this paper, we conduct a forensic analysis of two different Fitbit devices using open-source tools. It is the responsibility of the investigator to show how the data was obtained and to ensure that the data was not modified during the analysis. This paper will guide investigators in understanding what data is collected by a Fitbit device (specifically the Ionic smartwatch and Alta tracker), how to handle Fitbit devices, and how to extract and forensically analyze said devices using open-source tools, Autopsy Sleuth Kit and Bulk Extractor Viewer.

Security has become a major concern for women, children and even elders in every walk of their life. Women are getting assaulted and molested, children are getting kidnapped, elder citizens are also facing many problems like robbery, etc. In this paper, a smart security solution called smart wearable device system is implemented using the Raspberry Pi3 for enhancing the safety and security of women/children. It works as an alert as well as a security system. It provides a buzzer alert alert to the people who are nearby to the user (wearing the smart device). The system uses Global Positioning System (GPS) to locate the user, sends the location of the user through SMS to the emergency contact and police using the Global System for Mobile Communications (GSM) / General Radio Packet Service (GPRS) technology. The device also captures the image of the assault and surroundings of the user or victim using USB Web Camera interfaced to the device and sends it as an E-mail alert to the emergency contact soon after the user presses the panic button present on Smart wearable device system.

Child security in the school campus is most important in building a good society. In and around the world the children are abused and killed also in sometimes by the people those who are not in good attitude in the school campus. To track and resolve such issues an enhanced security feature system is required. Hence in this paper an enhanced version of security system for children is proposed by using `Wearable Sensors'. In this proposed method two wearable sensors nodes such as `Staff Node' and `Student Node' are paired by using `Bluetooth' communication technology and Smart Watch technology is also used to communicate the Security Center or Processing Node for tracking them about their location and whether the two nodes are moved away from the classroom. If the child node is not moving for a long period then it may be notified by the center and they will inform the security officers near to the place. This proposed method may satisfy the need of school management about the staff movements with students and the behavior of students to avoid unexpected issues.

Mobile wearable health devices have expanded prevalent usage and become very popular because of the valuable health monitor system. These devices provide general health tips and monitoring human health parameters as well as generally assisting the user to take better health of themselves. However, these devices are associated with security and privacy risk among the consumers because these devices deal with sensitive data information such as users sleeping arrangements, dieting formula such as eating constraint, pulse rate and so on. In this paper, we analyze the significant security and privacy features of three very popular health tracker devices: Fitbit, Jawbone and Google Glass. We very carefully analyze the devices' strength and how the devices communicate and its Bluetooth pairing process with mobile devices. We explore the possible malicious attack through Bluetooth networking by hacker. The outcomes of this analysis show how these devices allow third parties to gain sensitive information from the device exact location that causes the potential privacy breach for users. We analyze the reasons of user data security and privacy are gained by unauthorized people on wearable devices and the possible challenge to secure user data as well as the comparison of three wearable devices (Fitbit, Jawbone and Google Glass) security vulnerability and attack type.

Smartwatches are expected to become the world's best-selling electronic product after smartphones. Various smart-watches have been released to the private consumer market, but the data on smartwatches is not well protected. In this paper, we show for the first time that photoplethysmography (PPG)signals influenced by hand gestures can be used to authenticate users on smartwatches. The insight is that muscle and tendon movements caused by hand gestures compress the arterial geometry with different degrees, which has a significant impact on the blood flow. Based on this insight, novel approaches are proposed to detect the starting point and ending point of the hand gesture from raw PPG signals and determine if these PPG signals are from a normal user or an attacker. Different from existing solutions, our approach leverages the PPG sensors that are available on most smartwatches and does not need to collect training data from attackers. Also, our system can be used in more general scenarios wherever users can perform hand gestures and is robust against shoulder surfing attacks. We conduct various experiments to evaluate the performance of our system and show that our system achieves an average authentication accuracy of 96.31 % and an average true rejection rate of at least 91.64% against two types of attacks.

Wearable devices, such as implantable medical devices and smart wearables, are becoming increasingly popular with applications that vary from casual activity monitoring to critical medical uses. Unsurprisingly, numerous security vulnerabilities have been found in this class of devices. Yet, research on physical measurement-based authentication and key distribution assumes that body-worn devices are benign and uncompromised. Tiek is a novel authentication and key distribution protocol which addresses this issue. We utilize two sources of randomness to perform device authentication and key distribution simultaneously but through separate means. This creates a two-tier authorization scheme that enables devices to join the network while protecting them from each other. We describe Tiek and analyze its security.

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.

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 or patterns) are the first choice of most consumers to authorize the payment. This paper 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, WristSpy, which examines to what extent the user's PIN/pattern during the mobile payment could be revealed from a single wrist-worn wearable device under different passcode input scenarios involving either two hands or a single hand. In particular, WristSpy has the capability to accurately reconstruct fine-grained hand movement trajectories and infer PINs/patterns when mobile and wearable devices are on two hands through building a Euclidean distance-based model and developing a training-free parallel PIN/pattern inference algorithm. When both devices are on the same single hand, a highly challenging case, WristSpy extracts multi-dimensional features by capturing the dynamics of minute hand vibrations and performs machine-learning based classification to identify PIN entries. Extensive experiments with 15 volunteers and 1600 passcode inputs demonstrate that an adversary is able to recover a user's PIN/pattern with up to 92% success rate within 5 tries under various input scenarios.

Wearable technology has been on an exponential rise and shows no signs of slowing down. One category of wearable technology is Fitness bands, which have the potential to show a user's activity levels and location data. Such information stored in fitness bands is just the beginning of a long trail of evidence fitness bands can store, which represents a huge opportunity to digital forensic practitioners. On the surface of recent work and research in this area, there does not appear to be any similar work that has already taken place on fitness bands and particularly, the devices in this study, a Garmin Forerunner 110, a Fitbit Charge HR and a Generic low-cost HETP fitness tracker. In this paper, we present our analysis of these devices for any possible digital evidence in a forensically sound manner, identifying files of interest and location data on the device. Data accuracy and validity of the evidence is shown, as a test run scenario wearing all of the devices allowed for data comparison analysis.