With the increased popularity of smartphones, various security threats and privacy leakages targeting them are discovered and investigated. In this work, we present SilentSense, a framework to authenticate users silently and transparently by exploiting dynamics mined from the user touch behavior biometrics and the micro-movement of the device caused by user's screen-touch actions. We build a "touch-based biometrics" model of the owner by extracting some principle features, and then verify whether the current user is the owner or guest/attacker. When using the smartphone, some unique operating dynamics of the user is detected and learnt by collecting the sensor data and touch events silently. When users are mobile, the micro-movement of mobile devices caused by touch is suppressed by that due to the large scale user-movement which will render the touch-based biometrics ineffective. To address this, we integrate a movementbased biometrics for each user with previous touch-based biometrics. We conduct extensive evaluations of our approaches on the Android smartphone, we show that the user identification accuracy is over 99%.

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Abstract-The smart grid introduces new privacy implications to individuals and their family due to the fine-grained usage data collection. For example, smart metering data could reveal highly accurate real-time home appliance energy load, which may be used to infer the human activities inside the houses. One effective way to hide actual appliance loads from the outsiders is Battery-based Load Hiding (BLH), in which a battery is installed for each household and smartly controlled to store and supply power to the appliances. Even though such technique has been demonstrated useful and can prevent certain types of attacks, none of existing BLH works can provide probably privacy-preserving mechanisms. In this paper, we investigate the privacy of smart meters via differential privacy. We first analyze the current existing BLH methods and show that they cannot guarantee differential privacy in the BLH problem. We then propose a novel randomized BLH algorithm which successfully assure differential privacy without considering realworld constraints, and further propose the Multitasking-BLHExp3 algorithm which adaptively update the BLH algorithm based on the context and the constraints. Results from extensive simulations show the efficiency and effectiveness of the proposed method over existing BLH methods.

Much research has been conducted to securely outsource multiple parties' data aggregation to an untrusted aggregator without disclosing each individual's privately owned data, or to enable multiple parties to jointly aggregate their data while preserving privacy. However, those works either require secure pair-wise communication channels or suffer from high complexity. In this paper, we consider how an external aggregator or multiple parties can learn some algebraic statistics (e.g., sum, product) over participants' privately owned data while preserving the data privacy. We assume all channels are subject to eavesdropping attacks, and all the communications throughout the aggregation are open to others. We propose several protocols that successfully guarantee data privacy under this weak assumption while limiting both the communication and computation complexity of each participant to a small constant.

Abstract-Cloud computing is a revolutionary computing paradigm which enables flexible, on-demand and low-cost usage of computing resources. Those advantages, ironically, are the causes of security and privacy problems, which emerge because the data owned by different users are stored in some cloud servers instead of under their own control. To deal with security problems, various schemes based on the Attribute-Based Encryption have been proposed recently. However, the privacy problem of cloud computing is yet to be solved. This paper presents an anonymous privilege control scheme AnonyControl to address not only the data privacy problem in a cloud storage, but also the user identity privacy issues in existing access control schemes. By using multiple authorities in cloud computing system, our proposed scheme achieves anonymous cloud data access and finegrained privilege control. Our security proof and performance analysis shows that AnonyControl is both secure and efficient for cloud computing environment.