Large language models have shown considerable abilities across many tasks, but their capacity to detect sensitive user information from text raises significant privacy concerns. While recent approaches have explored sanitizing text to hide private features, a deeper challenge remains: distinguishing true privacy preservation from deceptive transformations. In this paper, we investigate whether LLM-based sanitization reduces private feature leakage without misleading an adversary into confidently predicting incorrect labels. Using LLM as both sanitizer and adversary, we measure leakage using two entropy-based metrics: Empirical Average Objective Leakage (E-AOL) and Empirical Average Confidence Boost (E-ACB). These allow us to quantify not only how accurate adversarial predictions are, but also how confident they remain post-sanitization. We posit that deception, while reducing adversarial accuracy, will also increase confidence in incorrect inferences, and hence reduced accuracy alone should not be interpreted as true privacy. We show that while current LLMs can hide private features, their transformations sometimes cause deception. Finally, we evaluate the semantic utility of sanitized outputs using sentence embeddings, LLM-based similarity judgments, and standard metrics like BLEU and ROUGE. Our findings emphasize the importance of explicitly distinguishing between privacy and deception in LLM-based sanitization and provide a framework for evaluating this distinction under realistic adversarial conditions.

This paper analyzes the problem of estimating information leakage when the complete statistics of the privacy mechanism are not known, and the only available information consists of several input-output pairs obtained through interaction with the system or through some side channel. Several metrics, such as subjective leakage, objective leakage, and confidence boost, were introduced before for this purpose, but by design only work in a Bayesian framework. However, it is known that Bayesian inference can quickly become intractable if the domains of the involved variables are large. In this paper, we focus on this exact problem and propose a novel approach to perform an estimation of the leakage measures when the true knowledge of the privacy mechanism is beyond the reach of the user for a non-Bayesian framework using machine learning. Initially, we adapt the definition of leakage metrics to a non-Bayesian framework and derive their statistical bounds, and afterward, we evaluate the performance of those metrics via various experiments using Neural Networks, Random Forest Classifiers, and Support Vector Machines. We have also evaluated their performance on an image dataset to demonstrate the versatility of the metrics. Finally, we provide a comparative analysis between our proposed metrics and the metrics of the Bayesian framework.

Stealthy cyberattacks pose a significant threat to modern power systems by exploiting advanced techniques to manipulate system behavior while avoiding detection by traditional security measures. In this study, we focus on the impact of Deep Reinforcement Learning (DRL) based attackers on a sample microgrid and develop robust detectors to mitigate these threats. Leveraging an iterative training process, we enhance the capabilities of successive attackers and detectors, resulting in improved system security. Our experiments demonstrate that DRL-based attackers can effectively disrupt system operations, highlighting the importance of robust detection mechanisms. Subsequently, we develop robust detection mechanisms, making new attacker attempts unsuccessful. We show that detectors developed through our mechanism are more effective in mitigating system impact and quickly identifying anomalies.

We propose a novel problem formulation to address the privacy-utility tradeoff, specifically when dealing with two distinct user groups characterized by unique sets of private and utility attributes. Unlike previous studies that primarily focus on scenarios where all users share identical private and utility attributes and often rely on auxiliary datasets or manual annotations, we introduce a collaborative data-sharing mechanism between two user groups through a trusted third party. This third party uses adversarial privacy techniques with our proposed data-sharing mechanism to internally sanitize data for both groups and eliminates the need for manual annotation or auxiliary datasets. Our methodology ensures that private attributes cannot be accurately inferred while enabling highly accurate predictions of utility features. Importantly, even if analysts or adversaries possess auxiliary datasets containing raw data, they are unable to accurately deduce private features. Additionally, our data-sharing mechanism is compatible with various existing adversarially trained privacy techniques. We empirically demonstrate the effectiveness of our approach using synthetic and real-world datasets, showcasing its ability to balance the conflicting goals of privacy and utility.

We investigate the application of large language models (LLMs), specifically GPT-4, to scenarios involving the tradeoff between privacy and utility in tabular data. Our approach entails prompting GPT-4 by transforming tabular data points into textual format, followed by the inclusion of precise sanitization instructions in a zero-shot manner. The primary objective is to sanitize the tabular data in such a way that it hinders existing machine learning models from accurately inferring private features while allowing models to accurately infer utility-related attributes. We explore various sanitization instructions. Notably, we discover that this relatively simple approach yields performance comparable to more complex adversarial optimization methods used for managing privacy-utility tradeoffs. Furthermore, while the prompts successfully obscure private features from the detection capabilities of existing machine learning models, we observe that this obscuration alone does not necessarily meet a range of fairness metrics. Nevertheless, our research indicates the potential effectiveness of LLMs in adhering to these fairness metrics, with some of our experimental results aligning with those achieved by well-established adversarial optimization techniques.

The inevitable leakage of privacy as a result of unrestrained disclosure of personal information has motivated extensive research on robust privacy-preserving mechanisms. However, existing research is mostly limited to solving the problem in a static setting with disregard for the privacy leakage over time. Unfortunately, this treatment of privacy is insufficient in practical settings where users continuously disclose their personal information over time resulting in an accumulated leakage of the users’ sensitive information. In this paper, we consider privacy leakage over a finite time horizon and investigate optimal strategies to maximize the utility of the disclosed data while limiting the finite-horizon privacy leakage. We consider a simple privacy mechanism that involves compressing the user’s data before each disclosure to meet the desired constraint on future privacy. We further motivate several algorithms to optimize the dynamic privacy-utility tradeoff and evaluate their performance via extensive synthetic performance tests.

The conventional information leakage metrics assume that an adversary has complete knowledge of the distribution of the mechanism used to disclose information correlated with the sensitive attributes of a system. The only uncertainty arises from the specific realizations that are drawn from this distribution. This assumption does not hold in various practical scenarios where an adversary usually lacks complete information about the joint statistics of the private, utility, and the disclosed data. As a result, the typical information leakage metrics fail to measure the leakage appropriately. In this paper, we introduce multiple new versions of the traditional information-theoretic leakage metrics, that aptly represent information leakage for an adversary who lacks complete knowledge of the joint data statistics, and we provide insights into the potential uses of each. We experiment on a real-world dataset to further demonstrate how the introduced leakage metrics compare with the conventional notions of leakage. Finally, we show how privacy-utility optimization problems can be formulated in this context, such that their solutions result in the optimal information disclosure mechanisms, for various applications.

Traditional approaches to authenticated key establishment include the use of PKI or trusted third parties. While certificate deployment is sub-optimal for large-scale, low-cost applications, the use of trusted third parties is subject to human error and leaked credentials. For this context, co-location can be a valuable resource, and it is often exploited through common randomness harvesting techniques, but these, in turn, suffer from low achievable rates and usually from restrictive assumptions about the environment. Recent techniques for exploiting co-location are based on the notion of quality time and rely on sophisticated throttled clue-issuing mechanisms that allow a device with enough time to spend in the vicinity of the transmitter to find a secret key by collecting enough consecutive clues. By contrast, attackers are afforded only limited time to listen to, or interact with, the clue transmitter. Previous work in this direction deals solely with passive attackers and uses high-overhead information throttling mechanisms. This paper introduces the active attacker model for the quality-time paradigm and proposes a simple solution, a Zeroconf Key Establishment Protocol (ZeroProKeS). Additionally, the paper shows how to efficiently expand the proposed protocol to adhere to any customized information transfer function between legitimate users.

Information leakage is usually defined as the logarithmic increment in the adversary’s probability of correctly guessing the legitimate user’s private data or some arbitrary function of the private data when presented with the legitimate user’s publicly disclosed information. However, this definition of information leakage implicitly assumes that both the privacy mechanism and the prior probability of the original data are entirely known to the attacker. In reality, the assumption of complete knowledge of the privacy mechanism for an attacker is often impractical. The attacker can usually have access to only an approximate version of the correct privacy mechanism, computed from a limited set of the disclosed data, for which they can access the corresponding un-distorted data. In this scenario, the conventional definition of leakage no longer has an operational meaning. To address this problem, in this article, we propose novel meaningful information-theoretic metrics for information leakage when the attacker has incomplete information about the privacy mechanism—we call them average subjective leakage , average confidence boost , and average objective leakage , respectively. For the simplest, binary scenario, we demonstrate how to find an optimized privacy mechanism that minimizes the worst-case value of either of these leakages.

Current mainstream biometric user authentication approaches are based on passive measurements of the subject's characteristics, and usually come with less-than-satisfactory accuracy. This paper takes a unique approach to biometric authentication. Specifically, instead of training a machine learning algorithm to recognize a legitimate user, the paper proposes a hybrid type of training, in which the legitimate user is also trained to use a customized instance of the machine. The user thus achieves a level of artificially-induced expertise to interact with the machine, which makes the user easier to recognize. We implement this concept in a mouse-based user authentication system, in which we produce customized machine instances by introducing an angle offset to the standard mouse. Human subjects then rely on their kinesthetic intelligence to achieve motor learning and visual-motor adaptation to the modified mouse. We design a 7-week IRB-approved experiment, collect data from 18 human subjects over this period, and evaluate the proposed approach with two existing state-of-the-art mouse-based authentication schemes. We find that, in both schemes, our approach significantly outperforms the baseline in which a regular unaltered mouse is used. Somewhat surprisingly, results also show that our approach improves the authentication performance even when both legitimate and non-legitimate users are trained to exactly the same instance of customized machine (i.e., the same mouse angle offset). In addition, we also observe that users can generally maintain their learned expertise even after one week of washout, which further demonstrates the practicality of the approach. Finally, we present a practical strategy to manage the enrollment of users in such a proposed system.

We propose an adversarial learning framework that deals with the privacy-utility tradeoff problem under two types of conditions: data-type ignorant, and data-type aware. Under data-type aware conditions, the privacy mechanism provides a one-hot encoding of categorical features, representing exactly one class, while under data-type ignorant conditions the categorical variables are represented by a collection of scores, one for each class. We use a neural network architecture consisting of a generator and a discriminator, where the generator consists of an encoder-decoder pair, and the discriminator consists of an adversary and a utility provider. Unlike previous research considering this kind of architecture, which leverages autoencoders (AEs) without introducing any randomness, or variational autoencoders (VAEs) based on learning latent representations which are then forced into a Gaussian assumption, our proposed technique introduces randomness and removes the Gaussian assumption restriction on the latent variables, only focusing on the end-to-end stochastic mapping of the input to privatized data. We test our framework on different datasets: MNIST, FashionMNIST, UCI Adult, and US Census Demographic Data, providing a wide range of possible private and utility attributes. We use multiple adversaries simultaneously to test our privacy mechanism - some trained from the ground truth data and some trained from the perturbed data generated by our privacy mechanism. Through comparative analysis, our results demonstrate better privacy and utility guarantees than the existing works under similar, data-type ignorant conditions, even when the latter are considered under their original restrictive single-adversary model.

Powered by new advances in sensor development and artificial intelligence, the decreasing cost of computation, and the pervasiveness of handheld computation devices, biometric user authentication (and identification) is rapidly becoming ubiquitous. Modern approaches to biometric authentication, based on sophisticated machine learning techniques, cannot avoid storing either trained-classifier details or explicit user biometric data, thus exposing users’ credentials to falsification. In this paper, we introduce a secure way to handle user-specific information involved with the use of artificial neural networks for biometric authentication. Our proposed architecture, called a Neural Fuzzy Extractor (NFE), allows the coupling of pre-existing classifiers with fuzzy extractors, through an artificial-neuralnetwork-based buffer called an expander, with minimal or no performance degradation. The NFE thus offers all the performance advantages of modern deep-learningbased classifiers and all the security of standard fuzzy extractors. We demonstrate the NFE retrofit of a few classic artificial neural networks, for simple biometric authentication scenarios.

Maximal leakage is usually defined as the logarithmic increment in the adversary’s probability of correctly guessing the legitimate user’s private data, or some arbitrary function of the private data, when presented with the legitimate user’s publicly disclosed information. However, this definition of maximal leakage implicitly assumes that the privacy mechanism, as well as the prior probability of the original data, are entirely known to the attacker. In reality, this assumption is often impractical. The attacker can usually have access to only an approximate version of the correct privacy mechanism, computed from a limited set of the disclosed data, for which she can access the corresponding un-distorted data. In this scenario, maximal leakage no longer has an operational meaning. To address this problem, in this paper, we propose two novel meaningful information-theoretic metrics for information leakage when the attacker has incomplete information about the privacy mechanism – we call them maximal subjective leakage and maximal objective leakage, respectively. For a simple, binary scenario, we demonstrate how to find the optimal privacy mechanism that minimizes the system’s information leakages.

Due to the recent popularity of online social networks, coupled with people’s propensity to disclose personal information in an effort to achieve certain gratifications, the problem of navigating the tradeoff between privacy and utility attracted a lot of recent interest and generated a rich body of research. A critical prerequisite to solving the problem is to appropriately capture the privacy and the utility aspects in the problem formulation. Most of the existing works focus on the notion of privacy, while utility loss is often treated as the undesirable but necessary distortion of the true data, introduced by the privacy mechanism. By contrast, we are interested in modeling utility differently, by associating it with specific attributes of a user, just like privacy is associated with specific private attributes in the literature. Our model of utility facilitates a better and more precise privacy mechanism. We further incorporate into our problem formulation a practical constraint on acceptable loss in utility per unit gain in privacy, which allows users to customize the privacy mechanisms in order to account for the relative values that each user associates with their own privacy and utility. This paper discusses the intricacies of our utility model and the corresponding privacy-utility tradeoff, introduces a heuristic greedy algorithm to solve the problem and presents experimental results.

With the increasing use of online social networks as a source of news and information, the propensity for a rumor to disseminate widely and quickly poses a great concern, especially in disaster situations where users do not have enough time to fact-check posts before making the informed decision to react to a post that appears to be credible. At the same time, we know that misinformation is easily detectable by a certain few, very skeptical, or very informed users. In this study, we demonstrate how blending artificial intelligence and human skills can create a new paradigm for credibility prediction. The crowdsourcing part of the detection mechanism is implemented implicitly, by simply observing the natural interaction between users encountering the messages. Specifically, we explore the spread of information on Twitter at the microscopic (user-to-user propagation) level and propose a model that predicts if a message is True or False by observing the latent attributes of the message, along with those of the users interacting with it, and their reactions to the message. We demonstrate the application of this model to the detection of misinformation and rank the relevant message and user features that are most critical in influencing the spread of rumor over the network. Our experiments using real-world data show that the proposed model achieves over 90% accuracy in predicting the credibility of posts on Twitter, a significant boost over state-of-the-art models.

Biometric user authentication is at the core of multifactor authentication, and mouse-based biometric authentication comes at no additional cost for most computer systems. This paper describes a mouse-based user authentication scheme, called MAUSPAD, which uses a novel progress-adjusted dynamic time warping (PADTW) algorithm, along with a segmentation algorithm, to accurately and meaningfully measure the differences between observed data and reference data. By introducing a new concept, which we call progress, into standard DTW, the new PADTW can have better control of the warping and mapping process and hence is more suitable for comparing time-stamped spatial sequences such as mouse cursor movements. Furthermore, in order to preserve the important but transient details in the cursor movement (which may be critical in identifying a specific user), we apply a segmentation algorithm to divide each reference cursor movement into multiple smaller segments, and measure the differences between cursor movements at the segment level. Evaluation results on two mouse-behavior datasets show that MAUSPAD yields the best overall performance among tested schemes, and demonstrate the effectiveness of PADTW over DTW, and segmentation over non-segmentation. The processing techniques developed herein can be extended to applications that rely on sequence comparison, and where relevant sequence information spans multiple semantic domains.

Traditional approaches to secret key establishment based on common randomness have been based on certain restrictive assumptions, such as considering the available common randomness to consist of independent and identically distributed (i.i.d) repetitions of correlated random variables. Unfortunately, the i.i.d assumption does not generally reflect the conditions of real-life scenarios. For this reason, the current paper investigates the key-establishment potential of a more pragmatic model, in which all parties have access to imperfect information about a common source modeled as a Markov chain. Each party's information thus comes in the form of a hidden Markov model and, since the different parties share the same underlying Markov chain, we call the overall model a sibling hidden Markov model (SHMM). This paper studies upper and lower bounds on the secret key capacity for various types of SHMM. The difficulty of the problem emerges from its prohibitive computational cost. To address this obstacle, we represent the joint probability of the observations as the L1norm of a Markov random matrix, and use its convergence to a Lyapunov exponent.

The essence of information assurance resides in the ability to establish secret keys between the legitimate communicating parties. Common approaches to key establishment include public-key infrastructure, key-distribution centers, physical-layer security or key extraction from common randomness. Of these, the latter two are based on specific natural advantages that the legitimate parties hold over their adversaries -- most often, such advantages rely on superior or privileged communication channels. This paper tackles a key-establishment protocol that relies on a completely different type of advantage: time. The protocol builds on the idea that when two devices are able to spend a pre-determined, mostly uninterrupted, interval of time in the company of each other, and when such a feat is outside the capability of any realistic attacker, then the legitimate parties should be able to establish a secret key without any prior common information. The paper presents a basic efficient time-based key establishment protocol, and demonstrates how it can be extended to follow customized information transfer functions and deal with predictable fluctuations of wireless interference.

Secret key establishment from common randomness has been traditionally investigated under cartain limiting assumptions, of which the most ubiquitous appears to be that the information available to all parties comes in the form of independent and identically distributed (i.i.d.) samples of some correlated random variables. Unfortunately, models employing the i.i.d assumption are often not accurate representations of real scenarios. A more capable model would represent the available information as correlated hidden Markov models (HMMs), based on the same underlying Markov chain. Such a model accurately reflects the scenario where all parties have access to imperfect observations of the same source random process, exhibiting a certain time dependency. In this paper, we derive a computationally-efficient asymptotic converse bound for the secret key capacity of the correlated-HMM scenario. The main obstacle, not only for our model, but also for other non-i.i.d cases, is the computational complexity. We address this by converting the initial bound to a product of Markov random matrices, and using recent results regarding its convergence to a Lyapunov exponent. The methods developed in the paper are easily extensible to derive a secret-key capacity lower bound.

In the Cloud Computing paradigm, a user often reduces financial, personnel, and computational burdens by outsourcing computation and other IT services to a professional service provider. However, to be able to assure the correctness of the result, the user still needs to perform the verification himself. Such verification may be tedious and expensive. Consequently, users are likely to outsource (again) the verification workload to a third party. Other scenarios such as auditing and arbitrating may also require the use of third-party verification. Outsourcing verification will introduce new security challenges. One such challenge is to protect the computational task and the results from the untrusted third party verifier. In this work, we address this problem by proposing an efficient verification outsourcing scheme. To our knowledge, this is the first solution to the verification outsourcing problem. We show that, without using expensive fully-homomorphic encryption, an honest-but-curious third party can help to verify the result of an outsourced computational task without having to learn either the computational task or the result thereof. We have implemented our design by combining a novel commitment protocol and an additive-homomorphic encryption in the argument system model. The total cost of the verification in our design is less than the verifier's cost in the state-of-the-art argument systems that rely only on standard cryptographic assumptions.

In this paper, we study both topological and algebraic properties of unrooted Gaussian trees in order to characterize their security performance. Such performance is measured by the corresponding potential in extracting common randomness from a given tree, which is further determined by max-min and min-max conditional mutual information (CMI) values, subject to the order of selecting variables from the tree by legitimate nodes Alice and Bob, and an eavesdropper Eve, respectively. A new operation is proposed to transform a Gaussian tree into another, and also to order different Gaussian trees. Through such operation we construct several equivalent classes of Gaussian trees. Each class includes multiple Gaussian trees that can be partially ordered based on the associated max-min or min-max CMI metric, and thus, we can find the most secure and the least secure trees in each partially ordered set (poset). The union of all posets generates all possible non-isomorphic trees of the given number of variables. Then, we assign a particular polynomial to each Gaussian tree, and show that such polynomial can determine the relative security performance of the Gaussian tree with respect to other trees within the same class. In the end, based on a generalized integer partition method, we propose a novel approach to efficiently enumerate the most secure structures of all posets.

Establishing secret common randomness between two or multiple devices in a network resides at the root of communication security. In its most frequent form of key establishment, the problem is traditionally decomposed into a randomness generation stage (randomness purity is subject to employing often costly true random number generators) and an information-exchange agreement stage, which relies either on public-key infrastructure or on symmetric encryption (key wrapping). In this paper, we propose a secret-common-randomness establishment algorithm for ad hoc networks, which works by harvesting randomness directly from the network routing metadata, thus achieving both pure randomness generation and (implicitly) secret-key agreement. Our algorithm relies on the route discovery phase of an ad hoc network employing the dynamic source routing protocol, is lightweight, and requires relatively little communication overhead. The algorithm is evaluated for various network parameters in an OPNET ad hoc network simulator. Our results show that, in just 10 min, thousands of secret random bits can be generated network-wide, between different pairs in a network of 50 users.

In the cloud computing paradigm, clients outsource computation to professional service providers. However, service providers may be error-prone or otherwise not entirely trustworthy, and therefore oftentimes the returned results need to be thoroughly verified. As such, the problem of verifiable computation has been motivating a rapidly-growing body of research, yielding increasingly-efficient systems, which currently achieve nearly-practical verifiable computation. Most recent solutions firstly transform the computation task into an arithmetic circuit, and then based on this circuit they design a verification protocol using argument systems.

In this paper, our objective is to find out how topological and algebraic properties of unrooted Gaussian tree models determine their security robustness, which is measured by our proposed max-min information (MaMI) metric. Such metric quantifies the amount of common randomness extractable through public discussion between two legitimate nodes under an eavesdropper attack. We show some general topological properties that the desired max-min solutions shall satisfy. Under such properties, we develop conditions under which comparable trees are put together to form partially ordered sets (posets). Each poset contains the most favorable structure as the poset leader, and the least favorable structure. Then, we compute the Tutte-like polynomial for each tree in a poset in order to assign a polynomial to any tree in a poset. Moreover, we propose a novel method, based on restricted integer partitions, to effectively enumerate all poset leaders. The results not only help us understand the security strength of different Gaussian trees, which is critical when we evaluate the information leakage issues for various jointly Gaussian distributed measurements in networks, but also provide us both an algebraic and a topological perspective in grasping some fundamental properties of such models.

Due to the huge popularity of online social networks, many researchers focus on adding links, e.g., link prediction to help friend recommendation. So far, no research has been performed on link cuts. However, the spread of malware and misinformation can cause havoc and hence it is interesting to see how to cut links such that malware and misinformation will not run rampant. In fact, many online social networks can be modeled as undirected graphs. In this paper, we investigate different strategies to cut links among different users in undirected graphs so that the speed of virus and misinformation spread can be slowed down the most or even cut off. Two measures are chosen to evaluate the performance of these strategies: Average Inverse of Shortest Path Length (AIPL) and Rumor Saturation Rate (RSR). AIPL measures the communication efficiency of the whole graph while RSR checks the percentage of users receiving information within a certain time interval.

Extensive works have been undertaken to develop efficient statistical inference algorithms based on graphical models. However, there still lacks sufficient understanding about how topological properties affect certain information related metrics for certain graphs. In this paper, we are particularly interested in finding out how topological properties of rooted polytrees for Gaussian random variables determine its security robustness, which is measured by our proposed max-min information (MaMI) metric. MaMI is defined as the maximin value of the conditional mutual information between any two random variables (nodes) in a given DAG, conditioned on the value of a third random variable, which is at full disposal of an eavesdropper, under a constraint of a given fixed joint entropy. We show some general topological properties which the desired max-min solutions satisfy. Under such properties, we prove the superior max-min feature of the linear topology for a simple but non-trivial case. The results not only help us understand the security strength of different rooted polytree type DAGs, which is critical when we evaluate the information leakage issues for various jointly Gaussian distributed measurements in networks, but also provide us another algebraic and analysis perspective in grasping some fundamental properties of such DAGs.

In the Cloud Computing paradigm, a user often reduces financial, personnel, and computational burdens by outsourcing computation and other IT services to a professional service provider. However, to be able to assure the correctness of the result, the user still needs to perform the verification himself. Such verification may be tedious and expensive. Consequently, users are likely to outsource (again) the verification workload to a third party. Other scenarios such as auditing and arbitrating may also require the use of thirdparty verification. Outsourcing verification will introduce new security challenges. One such challenge is to protect the computational task and the results from the untrusted third party verifier. In this work, we address this problem by proposing an efficient verification outsourcing scheme. To our knowledge, this is the first solution to the verification outsourcing problem. We show that, without using expensive fully-homomorphic encryption, an honest-but-curious third party can help to verify the result of an outsourced computational task without having to learn either the computational task or the result thereof. We have implemented our design by combining a novel commitment protocol and an additive-homomorphic encryption in the argument system model. The total cost of the verification in our design is less than the verifier's cost in the state-of-the-art argument systems that rely only on standard cryptographic assumptions.

In this paper, we study the problem of half-duplex active eavesdropping in fast-fading channels. The active eavesdropper is a more powerful adversary than the classical eavesdropper. It can choose between two functional modes: eavesdropping the transmission between the legitimate parties (Ex mode), and jamming it (Jx mode)-the active eavesdropper cannot function in full duplex mode. We consider a conservative scenario, when the active eavesdropper can choose its strategy based on the legitimate transmitter-receiver pair's strategy, and thus, the transmitter and legitimate receiver have to plan for the worst. We show that conventional physical-layer secrecy approaches perform poorly (if at all), and we introduce a novel encoding scheme, based on very limited and unsecured feedback-the Block-Markov Wyner encoding scheme-which outperforms any schemes currently available.

In this paper, we propose a feedback scheme for transmitting secret messages between two legitimate parties, over an eavesdropped communication link. Relative to Wyner's traditional encoding scheme, our feedback-based encoding often yields larger rate-equivocation regions and achievable secrecy rates. More importantly, by exploiting the channel randomness inherent in the feedback channels, our scheme achieves a strictly positive secrecy rate even when the eavesdropper's channel is less noisy than the legitimate receiver's channel. All channels are modeled as binary and symmetric. We demonstrate the versatility of our feedback-based encoding method by using it in three different configurations: the stand-alone configuration, the mixed configuration (when it combines with Wyner's scheme), and the reversed configuration. Depending on the channel conditions, significant improvements over Wyner's secrecy capacity can be observed in all configurations.

This paper studies the problem of active eavesdropping in fast fading channels. The active eavesdropper (Eve-A) is a more powerful adversary than the classical eavesdropper. It can choose between two functional modes: eavesdropping (Ex mode), and jamming (Jx mode)-Eve-A cannot function in full duplex mode. We consider the most conservative scenario, when the Eve-A can choose her strategy based on the legitimate transmitter-receiver pair's strategy-and thus the transmitter and legitimate receiver have to plan for the worst. We introduce a novel encoding scheme, based on very limited and unprotected feedback-the Block-Markov Wyner (BMW) encoding scheme-which outperforms any schemes currently available.

We study the problem of jamming in a fixed-rate wireless system over fast fading channels. Both transmitter and jammer are subject to long term (average) power constraints. Our jamming problem is formulated as a zero-sum game, with the probability of outage as pay-off function and power control functions as strategies. We consider both the case with full channel state information (CSI) at all parties (available from a training and feedback protocol), and the case when no CSI is fed back from the receiver. Nash equilibria of mixed strategies are found by solving the generalized form of an older problem dated back to Bell and Cover.

In this paper, we adopt outage probability (lambda-capacity) in fast fading channels as a pay-off function in a zero- sum game between a legitimate transceiver pair and an uncorrelated Gaussian jammer. The transmitter aims at minimizing the outage probability, while the jammer attempts to maximize the outage probability. We consider both peak (over each codeword) and average (over all codewords) power constraints. For peak power constraints, a transmission rate is either supported by the system, or if too large, causes the whole transmission to fail. By imposing average power constraints, large rates can be supported at the cost of positive probability of codeword error. Maxmin and minimax power control strategies are developed, which show that no Nash equilibrium of pure strategies exists under average power constraints.

We formulate a Gaussian uncorrelated jamming problem in block fading channels under long term power constraints. Source aims at minimizing the outage probability of its transmission under the presence of a malicious jammer, while the jammer attempts to maximize the corresponding outage probability under its average power constraint. Optimal power control strategies for both source and jammer are obtained for minimax and maxmin problems, respectively, for any arbitrary finite number of blocks in block fading channels. Our results demonstrate the non-existence of Nash-equilibria of this two- person zero-sum game.