In dynamic spectrum access (DSA), secondary users (SU) should only be allowed to access a licensed band belonging to incumbent users (IU) when the quality-of-service (QoS) requirements of both IUs and SUs can be satisfied at the same time. However, IU’s location and its received interference strength are considered sensitive in many DSA systems which should not be revealed, making it very challenging to optimize the network utility subjected to satisfying the operation and security requirements of SUs and IUs. In this paper, we develop a secure and distributed SU transmit power control algorithm to solve this challenge. Our algorithm achieves optimal SU power control to maximize the sum of SU rates. The SINR-guaranteed coexistence between SUs and IUs are enabled to maintain effective communication, while no information is directly required from IUs. Local measurements of IU signals provided by Environmental sensing capability (ESC) also undergo a security masking process to ensure that IU location cannot be derived from its outputs. Convergence and stability properties of our algorithm and its privacy-protection strength are both theoretically analyzed and experimentally evaluated through simulations
more »
« less
DPavatar: A Real-time Location Protection Framework for Incumbent Users in Cognitive Radio Networks
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
- NSF-PAR ID:
- 10112310
- Date Published:
- Journal Name:
- IEEE Transactions on Mobile Computing
- ISSN:
- 1536-1233
- Page Range / eLocation ID:
- 1 to 1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
In dynamic spectrum access (DSA), Environmental Sensing Capability (ESC) systems are implemented to detect the incumbent users' (IU) activities for protecting them from secondary users' (SU) interference as well as maximizing secondary spectrum usage. However, IU location information is often highly sensitive and hence it is preferable to hide its true location under the detection of ESCs. In this paper, we design novel schemes to preserve both static and moving IU's location information by adjusting IU's radiation pattern and transmit power. We first formulate IU privacy protection problem for static IU. Due to the intractable nature of this problem, we propose a heuristic approach based on sampling. We also formulate the privacy protection problem for moving IUs, in which two cases are analyzed: (1) protect IU's moving traces; (2) protect its real-time current location information. Our analysis provides insightful advice for IU to preserve its location privacy against ESCs. Simulation results show that our approach provides great protection for IU's location privacy.more » « less
-
The emergence of mobile apps (e.g., location-based services, geo-social networks, ride-sharing) led to the collection of vast amounts of trajectory data that greatly benefit the understanding of individual mobility. One problem of particular interest is next-location prediction, which facilitates location-based advertising, point-of-interest recommendation, traffic optimization,etc. However, using individual trajectories to build prediction models introduces serious privacy concerns, since exact whereabouts of users can disclose sensitive information such as their health status or lifestyle choices. Several research efforts focused on privacy-preserving next-location prediction, but they have serious limitations: some use outdated privacy models (e.g., k-anonymity), while others employ learning models with limited expressivity (e.g., matrix factorization). More recent approaches(e.g., DP-SGD) integrate the powerful differential privacy model with neural networks, but they provide only generic and difficult-to-tune methods that do not perform well on location data, which is inherently skewed and sparse.We propose a technique that builds upon DP-SGD, but adapts it for the requirements of next-location prediction. We focus on user-level privacy, a strong privacy guarantee that protects users regardless of how much data they contribute. Central to our approach is the use of the skip-gram model, and its negative sampling technique. Our work is the first to propose differentially-private learning with skip-grams. In addition, we devise data grouping techniques within the skip-gram framework that pool together trajectories from multiple users in order to accelerate learning and improve model accuracy. Experiments conducted on real datasets demonstrate that our approach significantly boosts prediction accuracy compared to existing DP-SGD techniques.more » « less
-
The emergence of mobile apps (e.g., location-based services,geo-social networks, ride-sharing) led to the collection of vast amounts of trajectory data that greatly benefit the understanding of individual mobility. One problem of particular interest is next-location prediction, which facilitates location-based advertising, point-of-interest recommendation, traffic optimization,etc. However, using individual trajectories to build prediction models introduces serious privacy concerns, since exact whereabouts of users can disclose sensitive information such as their health status or lifestyle choices. Several research efforts focused on privacy-preserving next-location prediction, but they have serious limitations: some use outdated privacy models (e.g., k-anonymity), while others employ learning models with limited expressivity (e.g., matrix factorization). More recent approaches(e.g., DP-SGD) integrate the powerful differential privacy model with neural networks, but they provide only generic and difficult-to-tune methods that do not perform well on location data, which is inherently skewed and sparse.We propose a technique that builds upon DP-SGD, but adapts it for the requirements of next-location prediction. We focus on user-level privacy, a strong privacy guarantee that protects users regardless of how much data they contribute. Central toour approach is the use of the skip-gram model, and its negative sampling technique. Our work is the first to propose differentially-private learning with skip-grams. In addition, we devise data grouping techniques within the skip-gram framework that pool together trajectories from multiple users in order to acceleratelearning and improve model accuracy. Experiments conducted on real datasets demonstrate that our approach significantly boosts prediction accuracy compared to existing DP-SGD techniques.more » « less
-
Reinforcement learning (RL) presents numerous benefits compared to rule-based approaches in various applications. Privacy concerns have grown with the widespread use of RL trained with privacy- sensitive data in IoT devices, especially for human-in-the-loop systems. On the one hand, RL methods enhance the user experience by trying to adapt to the highly dynamic nature of humans. On the other hand, trained policies can leak the user’s private information. Recent attention has been drawn to designing privacy-aware RL algorithms while maintaining an acceptable system utility. A central challenge in designing privacy-aware RL, especially for human-in-the-loop systems, is that humans have intrinsic variability, and their preferences and behavior evolve. The effect of one privacy leak mitigation can differ for the same human or across different humans over time. Hence, we can not design one fixed model for privacy-aware RL that fits all. To that end, we propose adaPARL, an adaptive approach for privacy-aware RL, especially for human-in-the-loop IoT systems. adaPARL provides a personalized privacy-utility trade-off depend- ing on human behavior and preference. We validate the proposed adaPARL on two IoT applications, namely (i) Human-in-the-Loop Smart Home and (ii) Human-in-the-Loop Virtual Reality (VR) Smart Classroom. Results obtained on these two applications validate the generality of adaPARL and its ability to provide a personalized privacy-utility trade-off. On average, adaPARL improves the utility by 57% while reducing the privacy leak by 23% on average.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TMC.2019.2897099
