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1. A Design Space for Privacy Choices: Towards Meaningful Privacy Control in the Internet of Things

   https://doi.org/10.1145/3411764.3445148  

   Feng, Yuanyuan ; Yao, Yaxing ; Sadeh, Norman ( May 2021 , Proceedings of the 2021 CHI Conference on Human Factors in computing Systems)

   null (Ed.)

   “Notice and choice” is the predominant approach for data privacy protection today. There is considerable user-centered research on providing effective privacy notices but not enough guidance on designing privacy choices. Recent data privacy regulations worldwide established new requirements for privacy choices, but system practitioners struggle to implement legally compliant privacy choices that also provide users meaningful privacy control. We construct a design space for privacy choices based on a user-centered analysis of how people exercise privacy choices in real-world systems. This work contributes a conceptual framework that considers privacy choice as a user-centered process as well as a taxonomy for practitioners to design meaningful privacy choices in their systems. We also present a use case of how we leverage the design space to finalize the design decisions for a real-world privacy choice platform, the Internet of Things (IoT) Assistant, to provide meaningful privacy control in the IoT. 

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2. A Design Space for Privacy Choices: Towards Meaningful Privacy Control in the Internet of Things

   Feng, Y. ; Yao, Y. ; Sadeh, N. ( January 2021 , ACM Conference on Human Factors in Computing Systems)

   null (Ed.)

   “Notice and choice” is the predominant approach for data privacy protection today. There is considerable user-centered research on providing efective privacy notices but not enough guidance on designing privacy choices. Recent data privacy regulations worldwide established new requirements for privacy choices, but system practitioners struggle to implement legally compliant privacy choices that also provide users meaningful privacy control. We construct a design space for privacy choices based on a user-centered analysis of how people exercise privacy choices in real-world systems. This work contributes a conceptual framework that considers privacy choice as a user-centered process as well as a taxonomy for practitioners to design meaningful privacy choices in their systems. We also present a use case of how we leverage the design space to fnalize the design decisions for a real-world privacy choice platform, the Internet of Things (IoT) Assistant, to provide meaningful privacy control in the IoT. 

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3. SenseHash: Computing on Sensor Values Mystified At the Origin

   https://doi.org/10.1109/TETC.2022.3217488  

   Sheybani, N ; Zhang, X ; Hussain, S. ; Koushanfar, F. ( November 2022 , IEEE transactions on emerging topics in computing)

   We propose SenseHash, a novel design for the lightweight in-hardware mystification of the sensed data at the origin. The framework aims to ensure the privacy of sensitive sensor values while preserving their utility. The sensors are assumed to interface to various (potentially malicious) communication and computing components in the Internet-of-things (IoT) and other emerging pervasive computing scenarios. The primary security primitives of our work are Locality Sensitive Hashing (LSH) combined with Differential Privacy (DP) and secure construction of LSH. Our construction allows (i) sub-linear search in sensor readings while ensuring their security against triangulation attack, and (ii) differentially private statistics of the readings. SenseHash includes hardware architecture as well as accompanying protocols to efficiently utilize the secure readings in practical scenarios. Alongside these scenarios, we present an automated workflow to generalize the application of the mystified readings. Proof-of-concept FPGA implementation of the system demonstrates its practicability and low overhead in terms of hardware resources, energy consumption, and protocol execution time. 

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4. Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix

   Lam, Maximillian ; Wei, Gu-Yeon ; Brooks, David ; Reddi, Vijay ; Mitzenmacher, Michael ( January 2021 , International Conference on Machine Learning)

   null (Ed.)

   We show that aggregated model updates in federated learning may be insecure. An untrusted central server may disaggregate user updates from sums of updates across participants given repeated observations, enabling the server to recover privileged information about individual users’ private training data via traditional gradient inference attacks. Our method revolves around reconstructing participant information (e.g: which rounds of training users participated in) from aggregated model updates by leveraging summary information from device analytics commonly used to monitor, debug, and manage federated learning systems. Our attack is parallelizable and we successfully disaggregate user updates on settings with up to thousands of participants. We quantitatively and qualitatively demonstrate significant improvements in the capability of various inference attacks on the disaggregated updates. Our attack enables the attribution of learned properties to individual users, violating anonymity, and shows that a determined central server may undermine the secure aggregation protocol to break individual users’ data privacy in federated learning. 

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5. Connecting the Dots: Tracing Data Endpoints in IoT Devices

   https://doi.org/10.56553/popets-2024-0090  

   Jakaria, Md ; Huang, Danny Yuxing ; Das, Anupam ( July 2024 , Proceedings on Privacy Enhancing Technologies)

   Smart home devices are constantly exchanging data with a variety of remote endpoints. This data encompasses diverse information, from device operation and status to sensitive user information like behavioral usage patterns. However, there is a lack of transparency regarding where such data goes and with whom it is potentially shared. This paper investigates the diverse endpoints that smart home Internet-of-Things (IoT) devices contact to better understand and reason about the IoT backend infrastructure, thereby providing insights into potential data privacy risks. We analyze data from 5,413 users and 25,123 IoT devices using the IoT Inspector, an open-source application allowing users to monitor traffic from smart home devices on their networks. First, we develop semi-automated techniques to map remote endpoints to organizations and their business types to shed light on their potential relationships with IoT end products. We discover that IoT devices contact more third or support-party domains than first-party domains. We also see that the distribution of contacted endpoints varies based on the user's location and across vendors manufacturing similar functional devices, where some devices are more exposed to third parties than others. Our analysis also reveals the major organizations providing backend support for IoT smart devices and provides insights into the temporal evolution of cross-border data-sharing practices. 

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