Privacy and authenticity of data pushed by or into wearable devices are of important concerns. Wearable devices equipped with various sensors can capture user's activity in fine-grained level. In this work, we investigate the possibility of using user's activity information to develop an implicit authentication approach for wearable devices. We design and implement a framework that does continuous and implicit authentication based on ambulatory activities performed by the user. The system is validated using data collected from 30 participants with wearable devices worn across various regions of the body. The evaluation results show that the proposed approach can achieve as high as 97% accuracy rate with less than 1% false positive rate to authenticate a user using a single wearable device. And the accuracy rate can go up to 99.6% when we use the fusion of multiple wearable devices.

Context-aware services nowadays offer incentive to user-reported context information , which inevitably solicits malicious users to cheat by submitting fabricated context claims. Conventional countermeasures based on Trusted Computing Base typically focus on particular context of interest, while disregarding the availability of various types of context information and the intrinsic correlation among them. In this work we propose a context claim verification scheme that interrogates correlated contexts of multiple dimensions to corroborate or contradict the reported context. Specifically, it first learns and models the context correlation with a Bayesian Multinet. Given a claim consisting of reported context and witnessing evidence, the scheme performs Bayesian inference with the evidence to verify the reported context. The verification process is light-weight, and can be applied to arbitrary types of context with a single model learnt. Evaluations on Reality Mining dataset and synthetic dataset validates choice of Multinet for data modeling, and demonstrate the feasibility of our scheme in context verification.

Location-based services are quickly becoming immensely popular. In addition to services based on users' current location, many potential services rely on users' location history, or their spatial-temporal provenance. Malicious users may lie about their spatial-temporal provenance without a carefully designed security system for users to prove their past locations. In this paper, we present the Spatial-Temporal provenance Assurance with Mutual Proofs (STAMP) scheme. STAMP is designed for ad-hoc mobile users generating location proofs for each other in a distributed setting. However, it can easily accommodate trusted mobile users and wireless access points. STAMP ensures the integrity and non-transferability of the location proofs and protects users' privacy. A semi-trusted Certification Authority is used to distribute cryptographic keys as well as guard users against collusion by a light-weight entropy-based trust evaluation approach. Our prototype implementation on the Android platform shows that STAMP is low-cost in terms of computational and storage resources. Extensive simulation experiments show that our entropy-based trust model is able to achieve high ( > 0.9) collusion detection accuracy.

Energy Expenditure (EE) Estimation is an important step in tracking personal activity and preventing chronic diseases such as obesity, diabetes and cardiovascular diseases. Accurate and online EE estimation using small wearable sensors is a difficult task, primarily because most existing schemes work offline or using heuristics. In this work, we focus on accurate EE estimation for tracking ambulatory activities (walking, standing, climbing upstairs or downstairs) of individuals wearing mobile sensors. We use Convolution Neural Networks (CNNs) to automatically detect important features from data collected from triaxial accelerometer and heart rate sensors. Using CNNs, we find a significant improvement in EE estimation compared to other state-of-the-art models. We compare our results against state-of-the-art Activity-Specific Linear Regression as well as Artificial Neural Networks (ANN) based models. Using a universal CNN model, we obtain an overall low Root Mean Square Error (RMSE) of 1.12 which is 30% and 35% lower than existing models. The results were calibrated against a COSMED K4b2 indirect calorimeter readings.

Many location-based services require a mobile user to continuously prove his location. In absence of a secure mechanism, malicious users may lie about their locations to get these services. Mobility trace, a sequence of past mobility points, provides evidence for the user's locations. In this paper, we propose a Continuous Location Integrity and Provenance (CLIP) Scheme to provide authentication for mobility trace, and protect users' privacy. CLIP uses low-power inertial accelerometer sensor with a light-weight entropy-based commitment mechanism and is able to authenticate the user's mobility trace without any cost of trusted hardware. CLIP maintains the user's privacy, allowing the user to submit a portion of his mobility trace with which the commitment can be also verified. Wireless Access Points (APs) or colocated mobile devices are used to generate the location proofs. We also propose a light-weight spatial-temporal trust model to detect fake location proofs from collusion attacks. The prototype implementation on Android demonstrates that CLIP requires low computational and storage resources. Our extensive simulations show that the spatial-temporal trust model can achieve high (> 0.9) detection accuracy against collusion attacks.

Indoor navigation is typically achieved by an operational localization system among a range of location-based services it provides. However, the construction of a localization map, which is prerequisite for binding sensed observation in sensing space to individual locations in geographic space, remains a challenging task to date. In this work, we propose an indoor navigation system that alleviates the need for constructing a localization map and instead provides navigation in signal space. The main idea behind our approach is to construct a sensing map consisting of signal observations (WiFi Clusters) and connecting dead-reckoning segments obtained through mobile sensing capabilities (traces of accelerometer and digital compass reading). To this end, we design a prototype to demonstrate effective construction of such sensing map with energy-efficient sensors and crowd-sourcing, and its ability to support accurate navigation.

Time-critical Location Based Service (LBS) applications in mobile ad hoc networks require fast localization. The conventional localization techniques are, unfortunately, unsuitable for such applications, for they neglect the time needed for localization. As a result, time-critical information may become obsolete, and the mobile users such as vehicles may have moved to new locations before the localization procedure is completed. To address this issue, we formulate a notion of On-Demand Fast Localization (ODFL) and devise a framework to implement this concept over existing routing protocols in MANETs. We present analytical and simulation results to demonstrate that ODFL can significantly reduce the time solely needed for localization before starting time-critical applications. Moreover, we show that ODFL can also improve location privacy and reduce energy consumptions.

Location-based services are quickly becoming immensely popular. In addition to services based on users' current location, many potential services rely on users' location history, or their spatial-temporal provenance. Malicious users may lie about their spatial-temporal provenance without a carefully designed security system for users to prove their past locations. In this paper, we present the Spatial-Temporal provenance Assurance with Mutual Proofs (STAMP) scheme. In contrast to most existing location proof systems which rely on infrastructure like wireless APs, STAMP is based on co-located mobile devices mutually generating location proofs for each other. This makes STAMP desirable for a wider range of applications. STAMP ensures the integrity and non-transferability of the location proofs and protects users' privacy. We also examine different collusion scenarios and propose a light-weight entropy-based trust evaluation approach to detect fake proofs resulting from collusion attacks. Our prototype implementation on the Android platform shows that STAMP is low-cost in terms of computational and storage resources. Extensive simulation experiments show that our entropy-based trust model is able to achieve high (> 0.9) collusion detection accuracy.

RSS (Received Signal Strength) has been widely utilized in wireless applications. It is, however, susceptible to environmental unknowns from both temporal and spatial domains. As a result, the fluctuation of RSS may degrade performance of RSS based applications. In this work, we propose a novel RSS processing method at the receiver for three antenna based systems. The output of our approach is `RSS-Ratio', which eliminates the environmental unknowns and thus is a more stable variable compared to RSS itself. To validate the efficacy of the proposed method, we conduct a series of experiments in a range of wireless scenarios, including indoor laptop based measurement, indoor software defined radio - WARP based measurement, and outdoor wireless measurement. In addition, we also give an analysis to the relationship between the location of transmitter and the value of RSS-Ratio, and examine the accuracy of the estimated RSS-Ratio value via both simulations and experiments. All the experimental, analytical, and simulated results demonstrate that RSS-Ratio will be a better replacement for RSS to improve the performance of RSS based applications.

Many popular mobile applications require the continuous monitoring and sharing of a mobile user's location. However, exploiting a user's location leads to disclosing sensitive information about the users daily activity. Several location privacy-preserving schemes have been proposed, but it remains challenging for a user to achieve visibility of the associated threats as well as to control the impact of those threats. This paper presents an adaptive location privacy-preserving system (ALPS) that allows for a user to control the level of privacy disclosure with different quality of location-based service (LBS). We have identified key attack models on location tracking using powerful map-matching algorithms, and then defined a scheme that allows a user to control the privacy of tracking information. We have implemented ALPS on Android OS and evaluated the implementation extensively via trace-based simulation, showing the effectiveness of user-controllable privacy preservation.

Crowd-sourced Wi-Fi-based localization systems utilize user input for RF scene analysis and map construction. Such systems reduce the deployment cost and privacy concerns that expert-based site survey systems can create. However, the main bottleneck of such crowd-sourcing localization systems is a bootstrapping stage, where lack of contributions by users results in no accuracy guarantee and frequent unnecessary prompting for users' input, even for explored areas. In this paper, we propose a crowd-sourcing localization system that uses both Wi-Fi scene analysis and Bluetooth beacons to address the insufficient contribution challenge. After prompting for user input, the mobile device not only submits Wi-Fi fingerprint to a map server, but also enables Bluetooth beacons to disseminate/share its location and fingerprint information to quickly populate the signal map. Then, subsequent user devices entering the area can discover the Bluetooth beacons and are able to instantly obtain room-level location information without causing unnecessary prompting to users. We implement our proposed system in the Linux OS and evaluate the prototype extensively through both experiments and simulation. Our evaluation results show that using Bluetooth beacons help to improve signal map growth, while maintaining reasonable localization accuracy.