More Like this

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. 

   more » « less
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. 

   more » « less
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. 

   more » « less
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. 

   more » « less
5. Safeguarding User-Centric Privacy in Smart Homes

   https://doi.org/10.1145/3701726  

   Yu, Keyang; Li, Qi; Chen, Dong; Hu, Liting (November 2024, ACM Transactions on Internet Technology)

   Internet of Things (IoT) devices have been increasingly deployed in smart homes to automatically monitor and control their environments. Unfortunately, extensive recent research has shown that on-path external adversaries can infer and further fingerprint people’s sensitive private information by analyzing IoT network traffic traces. In addition, most recent approaches that aim to defend against these malicious IoT traffic analytics cannot adequately protect user privacy with reasonable traffic overhead. In particular, these approaches often did not consider practical traffic reshaping limitations, user daily routine permitting, and user privacy protection preference in their design. To address these issues, we design a new low-cost, open source user-centric defense system—PrivacyGuard—that enables people to regain the privacy leakage control of their IoT devices while still permitting sophisticated IoT data analytics that is necessary for smart home automation. In essence, our approach employs intelligent deep convolutional generative adversarial network assisted IoT device traffic signature learning, long short-term memory based artificial traffic signature injection, and partial traffic reshaping to obfuscate private information that can be observed in IoT device traffic traces. We evaluate PrivacyGuard using IoT network traffic traces of 31 IoT devices from five smart homes and buildings. We find that PrivacyGuard can effectively prevent a wide range of state-of-the-art adversarial machine learning and deep learning based user in-home activity inference and fingerprinting attacks and help users achieve the balance between their IoT data utility and privacy preserving. 

   more » « less