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  1. Abstract Millions of consumers depend on smart camera systems to remotely monitor their homes and businesses. However, the architecture and design of popular commercial systems require users to relinquish control of their data to untrusted third parties, such as service providers (e.g., the cloud). Third parties therefore can (and in some instances have) access the video footage without the users’ knowledge or consent—violating the core tenet of user privacy. In this paper, we present CaCTUs , a privacy-preserving smart Camera system Controlled Totally by Users. CaCTUs returns control to the user ; the root of trust begins with the user and is maintained through a series of cryptographic protocols, designed to support popular features, such as sharing, deleting, and viewing videos live. We show that the system can support live streaming with a latency of 2 s at a frame rate of 10 fps and a resolution of 480 p. In so doing, we demonstrate that it is feasible to implement a performant smart-camera system that leverages the convenience of a cloud-based model while retaining the ability to control access to (private) data. 
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  4. Abstract Abstract: Users trust IoT apps to control and automate their smart devices. These apps necessarily have access to sensitive data to implement their functionality. However, users lack visibility into how their sensitive data is used, and often blindly trust the app developers. In this paper, we present IoTWATcH, a dynamic analysis tool that uncovers the privacy risks of IoT apps in real-time. We have designed and built IoTWATcH through a comprehensive IoT privacy survey addressing the privacy needs of users. IoTWATCH operates in four phases: (a) it provides users with an interface to specify their privacy preferences at app install time, (b) it adds extra logic to an app’s source code to collect both IoT data and their recipients at runtime, (c) it uses Natural Language Processing (NLP) techniques to construct a model that classifies IoT app data into intuitive privacy labels, and (d) it informs the users when their preferences do not match the privacy labels, exposing sensitive data leaks to users. We implemented and evaluated IoTWATcH on real IoT applications. Specifically, we analyzed 540 IoT apps to train the NLP model and evaluate its effectiveness. IoTWATcH yields an average 94.25% accuracy in classifying IoT app data into privacy labels with only 105 ms additional latency to an app’s execution. 
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