More Like this

1. Unclonable Secret Sharing

   Ananth, Prabhanjan; Goyal, Vipul; Liu, Jiahui; Liu, Qipeng (December 2024, Springer Nature)

   Unclonable cryptography utilizes the principles of quantum mechanics to addresses cryptographic tasks that are impossible classically. We introduce a novel unclonable primitive in the context of secret sharing, called unclonable secret sharing (USS). In a USS scheme, there are n shareholders, each holding a share of a classical secret represented as a quantum state. They can recover the secret once all parties (or at least t parties) come together with their shares. Importantly, it should be infeasible to copy their own shares and send the copies to two non-communicating parties, enabling both of them to recover the secret. Our work initiates a formal investigation into the realm of unclonable secret sharing, shedding light on its implications, constructions, and inherent limitations. Connections: We explore the connections between USS and other quantum cryptographic primitives such as unclonable encryption and position verification, showing the difficulties to achieve USS in different scenarios. Limited Entanglement: In the case where the adversarial shareholders do not share any entanglement or limited entanglement, we demonstrate information-theoretic constructions for USS. Large Entanglement: If we allow the adversarial shareholders to have unbounded entanglement resources (and unbounded computation), we prove that unclonable secret sharing is impossible. On the other hand, in the quantum random oracle model where the adversary can only make a bounded polynomial number of queries, we show a construction secure even with unbounded entanglement. Furthermore, even when these adversaries possess only a polynomial amount of entanglement resources, we establish that any unclonable secret sharing scheme with a reconstruction function implementable using Cliffords and logarithmically many T-gates is also unattainable. 

   more » « less
2. DPavatar: A Real-time Location Protection Framework for Incumbent Users in Cognitive Radio Networks

   https://doi.org/10.1109/TMC.2019.2897099  

   Liu, Jianqing; Zhang, Chi; Lorenzo, Beatriz; Fang, Yuguang (February 2019, IEEE Transactions on Mobile Computing)

   Dynamic spectrum sharing between licensed incumbent users (IUs) and unlicensed wireless industries has been well recognized as an efficient approach to solving spectrum scarcity as well as creating spectrum markets. Recently, both U.S. and European governments called a ruling on opening up spectrum that was initially licensed to sensitive military/federal systems. However, this introduces serious concerns on operational privacy (e.g., location, time and frequency of use) of IUs for national security concerns. Although several works have proposed obfuscation methods to address this problem, these techniques only rely on syntactic privacy models, lacking rigorous privacy guarantee. In this paper, we propose a comprehensive framework to provide real-time differential location privacy for sensitive IUs. We design a utility-optimal differentially private mechanism to reduce the loss in spectrum efficiency while protecting IUs from harmful interference. Furthermore, we strategically combine differential privacy with another privacy notion, expected inference error, to provide double shield protection for IU’s location privacy. Extensive simulations are conducted to validate our design and demonstrate significant improvements in utility and location privacy compared with other existing mechanisms. 

   more » « less
3. Privacy attitudes and data valuation among fitness tracker users

   Vitak, J.; Liao, Y.; Kumar, P.; Zimmer, M.; Kritikos, K. (March 2018, Lecture notes in computer science)

   Fitness trackers are an increasingly popular tool for tracking one’s health and physical activity. While research has evaluated the potential benefits of these devices for health and well-being, few studies have empirically evaluated users’ behaviors when sharing personal fitness information (PFI) and the privacy concerns that stem from the collection, aggregation, and sharing of PFI. In this study, we present findings from a survey of Fitbit and Jawbone users (N=361) to understand how concerns about privacy in general and user- generated data in particular affect users’ mental models of PFI privacy, tracking, and sharing. Findings highlight the complex relationship between users’ demographics, sharing behaviors, privacy concerns, and internet skills with how valuable and sensitive they rate their PFI. We conclude with a discussion of opportunities to increase user awareness of privacy and PFI. 

   more » « less
4. Privacy attitudes and data valuation among fitness tracker users

   https://doi.org/10.1007/978-3-319-78105-1_27  

   Vitak, J.; Liao, Y.; Kumar, P.; Zimmer, M.; Kritikos, K. (January 2018, Lecture notes in computer science)

   Fitness trackers are an increasingly popular tool for tracking one’s health and physical activity. While research has evaluated the potential benefits of these devices for health and well-being, few studies have empirically evaluated users’ behaviors when sharing personal fitness information (PFI) and the privacy concerns that stem from the collection, aggregation, and sharing of PFI. In this study, we present findings from a survey of Fitbit and Jawbone users (N=361) to understand how concerns about privacy in general and user- generated data in particular affect users’ mental models of PFI privacy, tracking, and sharing. Findings highlight the complex relationship between users’ demographics, sharing behaviors, privacy concerns, and internet skills with how valuable and sensitive they rate their PFI. We conclude with a discussion of opportunities to increase user awareness of privacy and PFI. 

   more » « less
5. Privacy-Preserving Object Detection with Secure Convolutional Neural Networks for Vehicular Edge Computing

   https://doi.org/10.3390/fi14110316  

   Bai, Tianyu; Fu, Song; Yang, Qing (November 2022, Future Internet)

   With the wider adoption of edge computing services, intelligent edge devices, and high-speed V2X communication, compute-intensive tasks for autonomous vehicles, such as object detection using camera, LiDAR, and/or radar data, can be partially offloaded to road-side edge servers. However, data privacy becomes a major concern for vehicular edge computing, as sensitive sensor data from vehicles can be observed and used by edge servers. We aim to address the privacy problem by protecting both vehicles’ sensor data and the detection results. In this paper, we present vehicle–edge cooperative deep-learning networks with privacy protection for object-detection tasks, named vePOD for short. In vePOD, we leverage the additive secret sharing theory to develop secure functions for every layer in an object-detection convolutional neural network (CNN). A vehicle’s sensor data is split and encrypted into multiple secret shares, each of which is processed on an edge server by going through the secure layers of a detection network. The detection results can only be obtained by combining the partial results from the participating edge servers. We have developed proof-of-concept detection networks with secure layers: vePOD Faster R-CNN (two-stage detection) and vePOD YOLO (single-stage detection). Experimental results on public datasets show that vePOD does not degrade the accuracy of object detection and, most importantly, it protects data privacy for vehicles. The execution of a vePOD object-detection network with secure layers is orders of magnitude faster than the existing approaches for data privacy. To the best of our knowledge, this is the first work that targets privacy protection in object-detection tasks with vehicle–edge cooperative computing. 

   more » « less