For $\tilde f(t) = \exp \left( {\frac{{\alpha - 1}}{\alpha }t} \right)$, this paper shows that the Sibson mutual information is an α-leakage averaged over the adversary’s $\tilde f$ -mean relative information gain (on the secret) at elementary event of channel output Y as well as the joint occurrence of elementary channel input X and output Y . This interpretation is used to derive a sufficient condition that achieves a δ-approximation of ϵ-upper bounded α-leakage. A Y -elementary α-leakage is proposed, extending the existing pointwise maximal leakage to the overall Rényi order range α ∈ [0,∞). Maximizing this Y -elementary leakage over all attributes U of channel input X gives the Rényi divergence. Further, the Rényi capacity is interpreted as the maximal $\tilde f$-mean information leakage over both the adversary’s malicious inference decision and the channel input X (represents the adversary’s prior belief). This suggests an alternating max-max implementation of the existing generalized Blahut-Arimoto method.

Pre-trained vision-language models (VLMs) exhibit significant vulnerability to imperceptible adversarial perturbations. Current advanced defense strategies typically employ adversarial prompt tuning to improve the adversarial robustness of VLMs, which struggle to simultaneously maintain generalization across both natural and adversarial examples under different benchmarks and downstream tasks. We propose a collaborative adversarial prompt tuning (CoAPT) approach from pre-trained VLMs to target robust VLMs. Inspired by the image mask modeling, we adopt an improved real-time total variation algorithm to suppress and eliminate high-frequency details from images while preserving edge structures, thereby disrupting the adversarial perturbation space. Subsequently, guided by the high-level image and text representations in the latent space of the pre-trained VLMs, the corrupted natural features are restored while inheriting the superior generalization capability. Experiments on four benchmarks demonstrate that CoAPT achieves an excellent trade-off among natural generalization, adversarial robustness, and task-specific adaptation compared to state-of-the-art methods.

Software applications, while being an integral part of the modern world, pose significant threats to end-user privacy. Thus, computer professionals require knowledge and skills to develop privacy-aware software. However, undergraduate computing degree programs often lack privacy-focused curricula that can cultivate this ability in the future workforce. Therefore, we designed a privacy curriculum informed by the common challenges that computing professionals face when developing privacy-embedded software. It guides students in realising the need for privacy, identifying privacy protection mechanisms and programming Privacy Enhancing Technologies (PETs). We piloted the curriculum for third-year Computer Science undergraduates at the University of Auckland, New Zealand. The curriculum was evaluated using course assessments and surveys conducted before and after the lessons. Overall, the students improved their understanding of privacy, especially technical aspects. Most of them valued the applied learning experience of the programming lessons yet showed distinct views on task completion difficulty and motivation to do programming. Students recognised that privacy should be integral to their skill set by confirming the importance and relevance of the lessons. However, their perceived responsibility in privacy protection varied depending on their intention to take proactive measures. Based on the results, the paper suggests improvements to the proposed curriculum.

This paper proposes an$\alpha$-leakage measure for$\alpha\in\lceil 0, \infty)$by a cross entropy interpretation of Renyi entropy. While Renyi entropy was originally defined as an f -mean for$f(t)=\exp((1-\alpha{)}t)$, we reveal that it is also a$\tilde{f}\cdot$mean cross entropy measure for$\vec{f}(t)=\exp \left(\frac{1-\alpha}{\alpha} t\right)$. Minimizing this Renyi cross-entropy gives Renyi entropy. This is used to define the prior and posterior uncertainty measures corresponding to the adversary's knowledge gain on sensitive attribute before and after data release, respectively. The a-leakage is proposed as the difference between$\hat{f}$-mean prior and posterior uncertainty measures, which is exactly the Arimoto mutual information. This not only extends the existing$\alpha$-leakage from$\alpha\in\lceil 1, \infty)$to the overall Renyi order range$\alpha\in\lceil 0, \infty)$in a well-founded way with$\alpha=0$referring to nonstochastic leakage, but also reveals that the existing maximal leakage is a$\tilde{f}\cdot$mean of an elementary$\alpha$-leakage for all$\alpha\in\lceil 0, \infty)$, generalizing the existing pointwise maximal leakage.

This paper studies how to approximate pufferfish privacy when the adversary’s prior belief of the published data is Gaussian distributed. Using Monge’s optimal transport plan, we show that$(\epsilon , \delta )$-pufferfish privacy is attained if the additive Laplace noise is calibrated to the differences in mean and variance of the Gaussian distributions conditioned on every discriminative secret pair. A typical application is the private release of the summation (or average) query, for which sufficient conditions are derived for approximating$\epsilon $-statistical indistinguishability in individual’s sensitive data. The result is then extended to arbitrary prior beliefs trained by Gaussian mixture models (GMMs): calibrating Laplace noise to a convex combination of differences in mean and variance between Gaussian components attains$(\epsilon ,\delta )$-pufferfish privacy.

Pufferfish privacy achieves $\epsilon$-indistinguishability over a set of secret pairs in the disclosed data. This paper studies how to attain $\epsilon$-pufferfish privacy by exponential mechanism, an additive noise scheme that generalizes the Laplace noise. It is shown that the disclosed data is $\epsilon$-pufferfish private if the noise is calibrated to the sensitivity of the Kantorovich optimal transport plan. Such a plan can be obtained directly from the data statistics conditioned on the secret, the prior knowledge of the system. The sufficient condition is further relaxed to reduce the noise power. It is also proved that the Gaussian mechanism based on the Kantorovich approach attains the $\delta$-approximation of $\epsilon$-pufferfish privacy.

This paper is concerned with enhancing data utility in the privacy watchdog method for attaining information-theoretic privacy. For a specific privacy constraint, the watchdog method filters out the high-risk data symbols through applying a uniform data regulation scheme, e.g., merging all high-risk symbols together. While this method entirely trades the symbols resolution off for privacy, we show that the data utility can be greatly improved by partitioning the high-risk symbols set and individually privatizing each subset. We further propose an agglomerative merging algorithm that finds a suitable partition of high-risk symbols: it starts with a singleton high-risk symbol, which is iteratively fused with others until the resulting subsets are private. Numerical simulations demonstrate the efficacy of this algorithm in privately achieving higher utilities in the watchdog scheme.

This paper proposes a novel watchdog privatization scheme by generalizing local information privacy (LIP) to enhance data utility. To protect the sensitive features S correlated with some useful data X, LIP restricts the lift, the ratio of the posterior belief to the prior on S after and before accessing X. For each x, both maximum and minimum lift over sensitive features quantify the privacy risk of publishing this symbol and should be restricted for the privacy-preserving purpose. Previous works enforce the same bound for both max-lift and min-lift. However, empirical observations show that the min-lift is usually much smaller than the max-lift. In this work, we generalize the LIP definition to consider the unequal values of max and min lift, i.e., considering different bounds for max-lift and min-lift. This new definition is applied to the watchdog privacy mechanism. We demonstrate that the utility is enhanced under a given privacy constraint on local differential privacy. At the same time, the resulting max-lift is lower and, therefore, tightly restricts other privacy leakages, e.g., mutual information, maximal leakage, and α-leakage.

This paper considers the problem of publishing data$X$while protecting the correlated sensitive information$S$. We propose a linear method to generate the sanitized data$Y$with the same alphabet$\mathcal{Y}=\mathcal{X}$that attains local differential privacy (LDP) and log-lift at the same time. It is revealed that both LDP and log-lift are inversely proportional to the statistical distance between conditional probability$P_{Y\vert S}(x\vert s)$and marginal probability$P_{Y}(x)$: the closer the two probabilities are, the more private$Y$is. Specifying$P_{Y\vert S}(x\vert s)$that linearly reduces this distance$\vert P_{Y\vert S}(x\vert s)-P_{Y}(x)\vert =(1-\alpha)\vert P_{X\vert S}(x\vert s)-P_{X}(x)\vert, \forall s, x$for some$\alpha\in(0,1]$, we study the problem of how to generate$\mathrm{Y}$from the original data$S$and$X$. The Markov randomization/sanitization scheme$P_{Y\vert X}(x\vert x^{\prime})=P_{Y\vert S,X}(x\vert s,x^{\prime})$is obtained by solving linear equations. The optimal non-Markov sanitization, the transition probability$P_{Y\vert S,X}(x\vert s,x^{\prime})$that depends on$S$,, can be determined by maximizing the data utility subject to linear equality constraints on data privacy. We compute the solution for two linear utility function: the expected distance and total variance distance. It is shown that the non-Markov randomization significantly improves data utility and the marginal probability$P_{X}(x)$remains the same after the linear sanitization method:$P_{Y}(x)=P_{X}(x),\forall x\in \mathcal{X}$.

This paper proposes an$\alpha$-lift measure for data privacy and determines the optimal privatization scheme that minimizes the$\alpha$-lift in the watchdog method. To release useful data$X$that is correlated with sensitive data$S$, the ratio of the posterior belief to the prior belief on sensitive data with respect to the useful data is called ‘lift’, which quantifies privacy risk. The$\alpha$-lift denoted by$\ell_{\alpha}(x)$is proposed as the$L_{\alpha}$-norm of the lift for a given realization$x$. This is a tunable measure: when$\alpha < \infty$, each lift is weighted by its likelihood of appearing in the dataset (w.r.t. the marginal probability$p(s)$); for$\alpha=\infty,\ \alpha$-lift reduces to the existing maximum lift. To generate the sanitized data$Y$, we adopt the privacy watchdog method using$\alpha$-lift: obtain realizations of useful data such that the$\alpha$-lift is greater than a threshold$e^{\epsilon}$; apply a randomization mechanism to these ‘high-risk’ realizations, while all other realizations of$X$are published directly. For the resulting$\alpha$-lift denoted by$\ell_{\alpha}(y)$, it is shown that the Sibson mutual information$I_{\alpha}^{S}(S;Y)$is proportional to$\mathbb{E}[\ell_{\alpha}(y)]$. We further define a stronger privacy measure denoted$\overline{I}_{\alpha}^{S}(S;Y)$using the worst-case$\alpha$-lift:$\bar{I}_{\alpha}^{S}(S;Y)\propto\max\nolimits_{y}\ell_{\alpha}(y)$. We prove that the optimal watchdog randomization that minimizes both$I_{\alpha}^{S}(S;Y)$and$\overline{I}_{\alpha}^{S}(S;Y)$is$X$-invariant. Numerical experiments show that$\alpha$-lift can provide flexibility in the privacy-utility tradeoff.

Communication for omniscience (CO) refers to the problem where the users in a finite set V observe a discrete multiple random source and want to exchange data over broadcast channels to reach omniscience, the state where everyone recovers the entire source. This paper studies how to improve the computational complexity for the problem of minimizing the sum-rate for attaining omniscience in V. While the existing algorithms rely on the submodular function minimization (SFM) techniques and complete in O(|V|2· SFM (|V|) time, we prove the strict strong map property of the nesting SFM problem. We propose a parametric (PAR) algorithm that utilizes the parametric SFM techniques and reduces the complexity to O(|V| · SFM (|V|). We propose efficient solutions to the successive omniscience (SO): attaining omniscience successively in user subsets. We first focus on how to determine a complimentary subset X*\subsetneq V in the existing two-stage SO such that if the local omniscience in X*is reached first, the global omniscience whereafter can still be attained with the minimum sum-rate. It is shown that such a subset can be extracted at one of the iterations of the PAR algorithm. We then propose a novel multi-stage SO strategy: a nesting sequence of complimentary user subsets X*(1)\subsetneq ...\subsetneq X*(K)= V, the omniscience in which is attained progressively by the monotonic rate vectorsrV(1)≤ ...≤rV(K). We propose algorithms to obtain this K-stage SO from the returned results by the PAR algorithm. The run time of these algorithms is the same as the PAR algorithm.

This paper studies the tradeoff in privacy and utility in a single-trial multi-terminal guessing (estimation) framework using a system model that is inspired by index coding. There are n independent discrete sources at a data curator. There are m legitimate users and one adversary, each with some side information about the sources. The data curator broadcasts a distorted function of sources to legitimate users, which is also overheard by the adversary. In terms of utility, each legitimate user wishes to perfectly reconstruct some of the unknown sources and attain a certain gain in the estimation correctness for the remaining unknown sources. In terms of privacy, the data curator wishes to minimize the maximal leakage: the worst-case guessing gain of the adversary in estimating any target function of its unknown sources after receiving the broadcast data. Given the system settings, we derive fundamental performance lower bounds on the maximal leakage to the adversary, which are inspired by the notion of confusion graph and performance bounds for the index coding problem. We also detail a greedy privacy enhancing mechanism, which is inspired by the agglomerative clustering algorithms in the information bottleneck and privacy funnel problems.

We propose an operational measure of information leakage in a non-stochastic setting to formalize privacy against a brute-force guessing adversary. We use uncertain variables, non-probabilistic counterparts of random variables, to construct a guessing framework in which an adversary is interested in determining private information based on uncertain reports. We consider brute-force trial-and-error guessing in which an adversary can potentially check all the possibilities of the private information that are compatible with the available outputs to find the actual private realization. The ratio of the worst-case number of guesses for the adversary in the presence of the output and in the absence of it captures the reduction in the adversary’s guessing complexity and is thus used as a measure of private information leakage. We investigate the relationship between the newly-developed measure of information leakage with maximin information and stochastic maximal leakage that are shown to arise in one-shot guessing.

We study the problem of privacy preservation in data sharing, where S is a sensitive variable to be protected and X is a non-sensitive useful variable correlated with S. Variable X is randomized into variable Y, which will be shared or released according to pY |X(y|x). We measure privacy leakage by information privacy (also known as log-lift in the literature), which guarantees mutual information privacy and differential privacy (DP). Let ${\mathcal{X}}_\varepsilon ^c \subseteq {\mathcal{X}}$ contain elements in the alphabet of for which the absolute value of log-lift (abs-log-lift for short) is greater than a desired threshold ϵ. When elements $x \in {\mathcal{X}}_\varepsilon ^c$ are randomized into $y \in {\mathcal{Y}},$ we derive the best upper bound on the abs-log-lift across the resultant pairs (s, y). We then prove that this bound is achievable via an X-invariant randomization p(y|x) = R(y) for $x,y \in {\mathcal{X}}_\varepsilon ^c$. However, the utility measured by the mutual information I(X; Y) is severely damaged in imposing a strict upper bound ϵ on the abs-log-lift. To remedy this and inspired by the probabilistic (ϵ, δ)-DP, we propose a relaxed (ϵ, δ)-log-lift framework. To achieve this relaxation, we introduce a greedy algorithm which exempts some elements in ${\mathcal{X}}_\varepsilon ^c$ from randomization, as long as their abs-log-lift is bounded by ϵ with probability 1 − δ. Numerical results demonstrate efficacy of this algorithm in achieving a better privacy-utility tradeoff.

We consider a non-stochastic privacy-preserving problem in which an adversary aims to infer sensitive information S from publicly accessible data X without using statistics. We consider the problem of generating and releasing a quantization X̂ of X to minimize the privacy leakage of S to X̂ while maintaining a certain level of utility (or, inversely, the quantization loss). The variables S and X are treated as bounded and non-probabilistic, but are otherwise general. We consider two existing non-stochastic privacy measures, namely the maximum uncertainty reduction L0(S → X̂) and the refined information X̂) (also called the maximin information) of S. For each I (S; privacy measure, we propose a corresponding agglomerative clustering algorithm that converges to a locally optimal quantization solution X̂ by iteratively merging elements in the alphabet of X. To instantiate the solution to this problem, we consider two specific utility measures, the worst-case resolution of Xby observing X̂ and the maximal distortion of the released data X̂. We show that the value of the maximin information I (S; X̂) can be determined by dividing the confusability graph into connected subgraphs. Hence, I (S; X̂) can be reduced by merging nodes connecting subgraphs. The relation to the probabilistic information-theoretic privacy is also studied by noting that the Gács-Körner common information is the stochastic version of I and indicates the attainability of statistical indistinguishability.

For the relevant data S that nests in the observation X, the information bottleneck (IB) aims to encode X into X̂ in X, with order to maximize the extracted useful information I(S; X̂) with the minimum coding rate I(X; X). For the dual privacy funnel (PF) problem where S denotes the sensitive/private data, the goal is to minimize the privacy leakage I(S; X̂) while maintain a certain level of utility I(X; X̂). For both problems, we propose an efficient iterative agglomerative clustering algorithm based on the minimization of the difference of submodular functions (IAC-MDSF). It starts with the original alphabet X̂ := X and iteratively merges the elements in the current alphabet X̂ that optimizes the Lagrangian function I(S; X̂)-λI(X; X̂). We prove that the best merge in each iteration of IAC-MDSF can be searched efficiently over all subsets of X̂ by the existing MDSF algorithms. By varying the value of the Lagrangian multiplier λ, we obtain the experimental results on a heart disease data set in terms of the Pareto frontier: I(S; X̂) vs. -I(X; X̂). We show that our IAC-MDSF algorithm outperforms the existing iterative pairwise merge approaches for both PF and IB and is computationally much less complex.

In this paper, the problem of self-organizing, correlation-aware clustering is studied for a dense network of machine-type devices (MTDs) deployed over a cellular network. In dense machine-to-machine networks, MTDs are typically located within close proximity and gather correlated data, and, thus, clustering MTDs based on data correlation leads to a decrease in the number of redundant bits transmitted to the base station. The clustering problem is formulated as an evolutionary game, which models the interactions among a massive number of MTDs, in order to decrease MTD transmission power. A novel utility function that captures the tradeoff between minimizing the average MTD transmission power per cluster and maximizing cluster size (or minimizing signaling overhead) is proposed. To solve this game, a distributed algorithm is proposed to allow a massive number of MTDs to autonomously form clusters. It is shown that the proposed distributed algorithm converges to an evolutionary stable strategy (ESS) that is robust to a small portion of MTDs deviating, e.g., due to some stochastic changes in the M2M environment from the stable cluster formation at convergence. The maximum fraction of MTDs that can deviate from the ESS, while still maintaining a stable cluster formation, is derived. Simulation results show the efficiency of the proposed algorithm in clustering MTDs with highly correlated data: on average, the proposed approach yields reductions of up to 44.1% and 15.25% in terms of the transmit power per cluster, compared to forming clusters with the maximum possible size and uniformly selecting a cluster size, respectively.

This paper proposes a novel splitting (SPLIT) algorithm to achieve fairness in the multiterminal lossless data compression problem. It finds the egalitarian solution in the Slepian-Wolf region and completes in strongly polynomial time. We show that the SPLIT algorithm adaptively updates the source coding rates to the optimal solution, while recursively splitting the terminal set, enabling parallel and distributed computation. The result of an experiment demonstrates a significant reduction in computation time by the parallel implementation when the number of terminals becomes large. The achieved egalitarian solution is also shown to be superior to the Shapley value in distributed networks, e.g., wireless sensor networks, in that it best balances the nodes' energy consumption and is far less computationally complex to obtain.

We consider the problem of how to attain fairness in the multiterminal data compression problem by a game-theoretic approach and present a decomposition method for obtaining the Shapley value, a fair source coding rate vector in the Slepian-Wolf achievable region. We model a discrete memoryless multiple random source (DMMS) by a coalitional game where the entropy function quantifies the cost incurred by the source coding rates in each coalition. In the typical case for which the game is decomposable, we show that the Shapley value can be obtained separately for each subgame. The complexity of this decomposition method is determined by the maximum size of subgames, which is strictly smaller than the total number of terminals in the DMMS and contributes to a considerable reduction in computational complexity. An experimental result demonstrates large complexity reduction when the number of terminals in the DMMS becomes large.

This paper considers the communication for omniscience problem: a set of users observe a discrete memoryless multiple source and want to recover the entire multiple source via noise-free broadcast communications. We study the problem of how to determine an optimal rate vector that attains omniscience with the minimum sum rate, the total number of communications. The results cover both asymptotic and non-asymptotic models where the transmission rates are real and integral, respectively. We propose a modified decomposition algorithm (MDA) and a sum-rate increment algorithm (SIA) for the asymptotic and non-asymptotic models, respectively, both of which determine the value of the minimum sum rate and a corresponding optimal rate vector in polynomial time. For the coordinate saturation capacity algorithm, a nesting algorithm in MDA and SIA, we propose to implement it by a fusion method and show by experimental results that this fusion method contributes to a reduction in computation complexity. Finally, we show that the separable convex minimization problem over the optimal rate vector set in the asymptotic model can be decomposed by the fundamental partition, the optimal partition of the user set that determines the minimum sum rate, so that the problem can be solved more efficiently.

The system that we study in this paper contains a set of users that observe a discrete memoryless multiple source and communicate via noise-free channels with the aim of attaining omniscience, the state that all users recover the entire multiple source. We adopt the concept of successive omniscience (SO), i.e., letting the local omniscience in some user subset be attained before the global omniscience in the entire system, and consider the problem of how to efficiently attain omniscience in a successive manner. Based on the existing results on SO, we propose a CompSetSO algorithm for determining a complimentary set, a user subset in which the local omniscience can be attained first without increasing the sum-rate, the total number of communications, for the global omniscience. We also derive a sufficient condition for a user subset to be complimentary so that running the CompSetSO algorithm only requires a lower bound, instead of the exact value of the minimum sum-rate for attaining global omniscience. The CompSetSO algorithm returns a complimentary user subset in polynomial time. We show by example how to recursively apply the CompSetSO algorithm so that the global omniscience can be attained by multi-stages of SO.

We consider the problem of how to fairly distribute the minimum sum-rate among the users in communication for omniscience (CO). We formulate a problem of minimizing a weighted quadratic function over a submodular base polyhedron which contains all achievable rate vectors, or transmission strategies, for CO that have the same sum-rate. By solving it, we can determine the rate vector that optimizes the Jain's fairness measure, a more commonly used fairness index than the Shapley value in communications engineering. We show that the optimizer is a lexicographically optimal (lex-optimal) base and can be determined by a decomposition algorithm (DA) that is based on submodular function minimization (SFM) algorithm and completes in strongly polynomial time. We prove that the lex-optimal minimum sum-rate strategy for CO can be determined by finding the lex-optimal base in each user subset in the fundamental partition and the complexity can be reduced accordingly.

This paper considers a cross-layer adaptive modulation system that is modeled as a Markov decision process. We study how to utilize the monotonicity of the optimal transmission policy to relieve the computational complexity of dynamic programming (DP). In this system, a scheduler controls the bit rate of the m-quadrature amplitude modulation in order to minimize the long-term losses incurred by the queue overflow in the data link layer and the transmission power consumption in the physical layer. The work is done in two steps. First, we observe the L#-convexity and submodularity of DP to prove that the optimal policy is always nondecreasing in queue occupancy/state and derive the sufficient condition for it to be nondecreasing in both queue and channel states. We also show that, due to the L#-convexity of DP, the variation of the optimal policy in queue state is restricted by a bounded marginal effect. The increment of the optimal policy between adjacent queue states is no greater than one. Second, we use the monotonicity results to present two low complexity algorithms: monotonic policy iteration (MPI) based on L#-convexity and discrete simultaneous perturbation stochastic approximation (DSPSA). We run experiments to show that the time complexity of MPI based on L#-convexity is much lower than that of DP and the conventional MPI that is based on submodularity and DSPSA is able to adaptively track the optimal policy when the system parameters change.

Because the exchange of information among all the users in a large network can take a long time, a successive omniscience protocol is proposed. Namely, subgroups of users first recover the information of other users in the same subgroup at an earlier stage called local omniscience. Then, the users recover the information of all other users at a later stage called global omniscience. To facilitate the information exchange, a distributed storage system is used, so that users can conveniently upload and download messages through some reliable central servers. The minimum upload bandwidth is characterized and a bandwidth-storage trade-off is discovered. The results reveal the new connections to the problem of secret key agreement and, consequently, provide meaningful interpretations of a recently proposed multivariate mutual information measure that was inspired by the secret key agreement problem.

This paper considers the discrete convexity of a cross-layer on-off transmission control problem in wireless communications. In this system, a scheduler decides whether or not to transmit in order to optimize the long-term quality of service (QoS) incurred by the queueing effects in the data link layer and the transmission power consumption in the physical (PHY) layer simultaneously. Using a Markov decision process (MDP) formulation, we show that the optimal policy can be determined by solving a minimization problem over a set of queue thresholds if the dynamic programming (DP) is submodular. We prove that this minimization problem is discrete convex. In order to search the minimizer, we consider two discrete stochastic approximation (DSA) algorithms: 1) discrete simultaneous perturbation stochastic approximation (DSPSA) and 2) L#-convex stochastic approximation (L#-convex SA). Through numerical studies, we show that the two DSA algorithms converge significantly faster than the existing continuous simultaneous perturbation stochastic approximation (CSPSA) algorithm in multiuser systems. Finally, we compare the convergence results and complexity of two DSA and CSPSA algorithms where we show that DSPSA achieves the best tradeoff between complexity and accuracy in multiuser systems.

This paper considers how to accurately estimate the minimum sum-rate so as to reduce the complexity of solving cooperative data exchange (CDE) problems. The CDE system contains a number of geographically close clients who send packets to help the others recover an entire packet set. The minimum sum-rate is the minimum value of total number of transmissions that achieves universal recovery (the situation when all the clients recover the whole packet set). Based on a necessary and sufficient condition for a supermodular base polyhedron to be nonempty, we show that the minimum sum-rate for a CDE system can be determined by a maximization over all possible partitions of the client set. Due to the high complexity of solving this maximization problem, we propose a deterministic algorithm to approximate a lower bound on the minimum sum-rate. We show by experiments that this lower bound is much tighter than those lower bounds derived in the existing literature. We also show that the deterministic algorithm prevents from repetitively running the existing algorithms for solving CDE problems so that the overall complexity can be reduced accordingly.

When designing two-way relay channels, there is a dilemma of how to reduce the transmission power by network coding (XORing symbols of opposite directions) with a low symbol delay. Moreover, if the channels are fading, an extra penalty caused by error probability should be added to each transmission, which makes the decision-making problem more complex. In this paper, we propose a new model that considers instantaneous signal to noise ratio (SNR) in addition to queue occupation status. In this model, the channel state evolution is traced by a finite state Markov chain. We develop an efficient computational solution utilizing value iteration algorithm to find an optimal policy regarding symbol delay, transmission power consumption, symbol loss due to the queue overflow and transmission error probabilities. Simulation results show that there is an improvement in both the symbol loss rate and the overall system cost in practical scenarios, compared to the conventional modeling method where channel states are ignored.

In this paper, we tackle client load estimation in a smart grid network. For that purpose, we propose an individual model based on nonparametric estimators. The model is designed for power estimations for use in network planning. Completely data-driven, the proposed methodology can be applied to both thermosensitive and nonthermosensitive clients. Real measurements collected in French distribution systems are used to validate our methodology. The proposed approach produces more reliable estimation results than the current model (termed BAGHEERA in this paper) of the French electricity company EDF does on the same data.

None of the features commonly utilised in automatic emotion classification systems completely disassociate emotion-specific information from speaker-specific information. Consequently, this speaker-specific variability adversely affects the performance of the emotion classification system and in existing systems is frequently mitigated by some form of speaker normalisation. Speaker adaptation offers an alternative to normalisation and this paper proposes a novel bootstrapping technique which involves selecting appropriate initial models from a large training pool, prior to speaker adaptation of emotion models in the context of GMM based emotion classification as an alternative to speaker normalisation. Evaluations on the LDC Emotional Prosody and the FAU Aibo corpora reveal that an emotion classification system based on the proposed bootstrapping method outperforms systems based on speaker normalisation as long as a small amount of labelled adaptation data is available. It also outperforms speaker adaption from common initial models estimated from all training speakers.

Speaker variability is a known challenge for emotion recognition, however little work has been done on speaker similarity in terms of its contribution to the performance in the emotion classification task. In this paper, we investigate this topic, and find a clear link between speaker proximity and the recognition accuracy. Motivated by this result, emotion based speaker clustering is proposed as a new strategy for speaker adaptation. It involves using speaker proximity to cluster individual speakers’ emotion models in the training set on a per-emotion basis, and adapting the test speaker’s emotion from the closest cluster. A series of tests were conducted to explore how system performance varies with clustering method, the number of clusters and the amount of adapting data. Results on the LDC Emotion Prosody and FAU Aibo Corpora show that this method outperforms speaker bootstrap, both in terms of relieving computation load and producing higher accuracy.