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To enhance the accuracy of learning models, it becomes imperative to train them on more extensive datasets. Unfortunately, access to such data is often restricted because data providers are hesitant to share their data due to privacy concerns. Hence, it is critical to develop obfuscation techniques that empower data providers to transform their datasets into new ones that ensure the desired level of privacy. In this paper, we present an approach where data providers utilize a neural network based on the autoencoder architecture to safeguard the sensitive components of their data while preserving the utility of the remaining parts. More specifically, within the autoencoder framework and after the encoding process, a classifier is used to extract the private feature from the dataset. This feature is then decorrelated from the other remaining features and subsequently subjected to noise. The proposed method is flexible, allowing data providers to adjust their desired level of privacy by changing the noise level. Additionally, our approach demonstrates superior performance in achieving the desired trade-off between utility and privacy compared to similar methods, all while maintaining a simpler structure. [ABSTRACT FROM AUTHOR]

Large-scale data collection is challenging in alternative centralized learning as privacy concerns or prohibitive policies may rise. As a solution, Federated Learning (FL) is proposed wherein data owners, called participants, can train a common model collaboratively while their privacy is preserved. However, recent attacks, namely Membership Inference Attacks (MIA) or Poisoning Attacks (PA), can threaten the privacy and performance in FL systems. This paper develops an innovative Adversarial-Resilient Privacy-preserving Scheme (ARPS) for FL to cope with preceding threats using differential privacy and cryptography. Our experiments display that ARPS can establish a private model with high accuracy out-performing state-of-the-art approaches. To the best of our knowledge, this work is the only scheme providing privacy protection beyond any output models in conjunction with Byzantine resiliency without sacrificing accuracy and efficiency. [ABSTRACT FROM AUTHOR]

We propose computationally inexpensive and efficient solutions for signal activity detection of phase-shift keying (PSK) signals in additive white Gaussian noise. We consider the complex amplitude of the signal, as well as the information sequence, as the unknown parameters. In addition, the noise variance is assumed unknown. We derive the generalized likelihood ratio test (GLRT) and suggest a computationally efficient implementation thereof. Furthermore, we develop a new inexpensive detector for binary PSK signals, which we will refer to as the generalized energy detector. To evaluate the performance of these detectors, we attempt to derive a uniformly most powerful invariant (UMPI) test as an optimal detector. It turns out that the UMPI test exists only if the signal-to-noise ratio is known. We use this UMPI test in order to obtain an upper-bound performance for the evaluation of invariant detectors, such as the GLRT. Simulation results illustrate and compare the performance and the efficiency of the proposed signal activity detectors. Index Terms--Additive Gaussian noise, binary detection problem, detection algorithms, generalized energy detector, generalized likelihood ratio (GLR), GLR detectors, invariances, invariant detectors, invariant hypothesis testing, matched filter detectors, maximal invariant statistic, maximum-likelihood (ML) detection, phase-shift keying (PSK), signal activity detection, signal classification, signal detection, uniformly most powerful invariant (UMPI) test.

ABSTRACT Ubiquitousness of Radio Frequency Identification (RFID) systems with inherent weaknesses has been a cause of concern about their privacy and security. Therefore, secure protocols are essentially necessary for the RFID tags to guarantee privacy and authentication among them and the reader. This paper inspects privacy in the RFID systems. First, we survey four new-found RFID authentication protocols, and then, their weaknesses in formal privacy model are analyzed. Although the authors of the schemes claimed that their protocols completely resist privacy attacks, we formally prove that all of them suffer from the family of traceability attacks. Furthermore, not only are the four improved protocols proposed to prevent the aforementioned attacks, but also we provide formal analysis to guarantee the security and privacy of the proposed enhancements. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, we propose a general mobility model suitable for wireless multimedia networks. Our model is based on splitting a region into subregions. Furthermore, we make an analogy between subregions as well as their inter-connections with a multi-class Jackson queueing network comprising of infinite-server nodes. The main attribute of such a network is due to its product-form stationary distribution. Using this model, we are able to obtain a closed analytical form for the spatial traffic distribution corresponding to a specific number of network-connected users with different classes of service and mobility in a typical region. Also, we show the flexibility obtained by the proposed mobility model in representing some general distributions such as sum-of-hyper-exponentials (SOHYP), hyper-Erlang and Cox which were previously suggested to model mobility-related statistical parameters, e.g., cell dwell time and channel holding time. Finally, we apply the proposed model to a few mobility scenarios and obtain the resultant active user's location density. [ABSTRACT FROM AUTHOR]