Privacy and security in cyber-physical systems

Data privacy has attracted increasing attention in the past decade due to the emerging technologies that require our data to provide utility. Service providers (SPs) encourage users to share their personal data in return for a better user experience. However, users' raw data usually contains implicit sensitive information that can be inferred by a third party. This raises great concern about users' privacy. In this dissertation, we develop novel techniques to achieve a better privacy-utility trade-off (PUT) in various applications. We first consider smart meter (SM) privacy and employ physical resources to minimize the information leakage to the SP through SM readings. We measure privacy using information-theoretic metrics and find private data release policies (PDRPs) by formulating the problem as a Markov decision process (MDP). We also propose noise injection techniques for time-series data privacy. We characterize optimal PDRPs measuring privacy via mutual information (MI) and utility loss via added distortion. Reformulating the problem as an MDP, we solve it using deep reinforcement learning (DRL) for real location trace data. We also consider a scenario for hiding an underlying ``sensitive'' variable and revealing a ``useful'' variable for utility by periodically selecting from among sensors to share the measurements with an SP. We formulate this as an optimal stopping problem and solve using DRL. We then consider privacy-aware communication over a wiretap channel. We maximize the information delivered to the legitimate receiver, while minimizing the information leakage from the sensitive attribute to the eavesdropper. We propose using a variational-autoencoder (VAE) and validate our approach with colored and annotated MNIST dataset. Finally, we consider defenses against active adversaries in the context of security-critical applications. We propose an adversarial example (AE) generation method exploiting the data distribution. We perform adversarial training using the proposed AEs and evaluate the performance against real-world adversarial attacks.Open Acces

Generative Joint Source-Channel Coding for Semantic Image Transmission

Recent works have shown that joint source-channel coding (JSCC) schemes using deep neural networks (DNNs), called DeepJSCC, provide promising results in wireless image transmission. However, these methods mostly focus on the distortion of the reconstructed signals with respect to the input image, rather than their perception by humans. However, focusing on traditional distortion metrics alone does not necessarily result in high perceptual quality, especially in extreme physical conditions, such as very low bandwidth compression ratio (BCR) and low signal-to-noise ratio (SNR) regimes. In this work, we propose two novel JSCC schemes that leverage the perceptual quality of deep generative models (DGMs) for wireless image transmission, namely InverseJSCC and GenerativeJSCC. While the former is an inverse problem approach to DeepJSCC, the latter is an end-to-end optimized JSCC scheme. In both, we optimize a weighted sum of mean squared error (MSE) and learned perceptual image patch similarity (LPIPS) losses, which capture more semantic similarities than other distortion metrics. InverseJSCC performs denoising on the distorted reconstructions of a DeepJSCC model by solving an inverse optimization problem using style-based generative adversarial network (StyleGAN). Our simulation results show that InverseJSCC significantly improves the state-of-the-art (SotA) DeepJSCC in terms of perceptual quality in edge cases. In GenerativeJSCC, we carry out end-to-end training of an encoder and a StyleGAN-based decoder, and show that GenerativeJSCC significantly outperforms DeepJSCC both in terms of distortion and perceptual quality.Comment: 12 pages, 9 figure

Hareketli çapaya dayalı kablosuz sensör ağlar için rota planlama ve konumlandırma.

In wireless sensor networks, sensors with limited resources are distributed in a wide area. Localizing the sensors is an important problem. Anchor nodes with known positions are used for sensor localization. A simple and efficient way of generating anchor nodes is to use mobile anchors which have built-in GPS units. In this thesis, a single mobile anchor is used to traverse the region of interest to communicate with the sensor nodes and identify their positions. Therefore planning the best trajectory for the mobile anchor is an important problem in this context. The mobile anchor stops on the trajectory to generate anchor nodes which are used in the position estimation of the unknown sensors. Various path planning methods for mobile anchors are proposed to localize as many sensors as possible by following the shortest path with minimum number of anchors. In this thesis, path planning and localization for mobile anchor based wireless sensor networks are investigated. Two novel path planning algorithms are proposed for static and dynamic schemes. These approaches use mobile anchors to cover the monitoring area with minimum path length and by stopping at minimum number of nodes. Moreover, alternating minimization algorithm is proposed for localizing the unknown sensor nodes non-cooperatively. The non-convex, NP-hard node localization problem is converted into a biconvex form and solved iteratively. The performances of the proposed path planning algorithms are compared with alternative approaches through simulations. The results show that more sensors are localized with less anchors in a shorter path and time for both schemes. Furthermore, alternating minimization algorithm provides an effective solution for the sensor localization problem. The simulation results show that the proposed localization approach is less prone to error accumulation than the alternative methods. M.S. - Master of Scienc

Path planning for mobile-anchor based wireless sensor network localization: Static and dynamic schemes

In wireless sensor networks, node locations are required for many applications. Usually, anchors with known positions are employed for localization. Sensor positions can be estimated more efficiently by using mobile anchors (MAs). Finding the best MA trajectory is an important problem in this context. Various path planning algorithms are proposed to localize as many sensors as possible by following the shortest path with minimum number of anchors. In this paper, path planning algorithms for MA assisted localization are proposed for both static and dynamic schemes. These approaches use MAs by stopping at minimum number of nodes to cover the monitoring area with shortest path length. A novel node localization algorithm based on alternating minimization is proposed. The performances of the proposed path planning algorithms are compared with previous approaches through simulations. The results show that more sensors are localized with less anchors in a shorter path and time for both schemes

Path Planning and Localization for Mobile Anchor Based Wireless Sensor Networks

In wireless sensor networks, anchor positions play an important role for accurate localization. For mobile anchor ( MA) based scenarios, both the efficiency of the path planning algorithm and the accuracy of the localization mechanism are critical for the best performance. In this work, an adaptive path planning algorithm is proposed based on Gauss-Markov mobility model, while the sensors are localized using alternating minimization approach. Path planning, which combines the velocity adjustment, the perpendicular bisector and the virtual repulsive strategies, is improved by developing virtual attractive force strategy. The surveillance area is divided into grids and a virtual attractive force is applied to the MA in sparsely and densely populated areas. For localization, the non-convex optimization problem is converted into a bi-convex form and solved by alternating minimization algorithm leading to a shorter MA path. The simulation results show that introducing the virtual attractive strategy increases the path planning accuracy and cover more surveillance region using less energy. Furthermore, compared to the linear localization method, the localization accuracy increases when the alternating minimization is used