arXiv subjects

Gerhard Wunder

Publications and source records attributed to Gerhard Wunder.

At least 37 records · Page 2Linked to original sources

arXiv2024

Perfectly Secure Key Agreement Over a Full Duplex Wireless Channel

Secret key generation (SKG) between authenticated devices is a pivotal task for secure communications. Diffie-Hellman (DH) is de-facto standard but not post-quantum secure. In this paper, we shall invent and analyze a new security primitive that is specifically designed for WPAN. For WPAN, wireless channel-based SKG has been proposed but was not widely deployed due to its critical dependence on the channel's entropy which is uncontrollable. We formulate a different approach: We still exploit channel properties but mainly hinge on the reciprocity of the wireless channel and not on the channel's entropy. The radio advantage comes from the use of full duplex communication. We show that in this situation both legitimate parties can agree on a common secret key even without ever probing the channel at all. At the core is a new bisparse blind deconvolution scheme for which we prove correctness and information-theoretic, i.e. perfect, security. We show that, ultimately, a secret key can be extracted and give a lower bound for the number of secret key bits which is then verified by experiments.

cs.IT↗

arXiv2024

ASOP: A Sovereign and Secure Device Onboarding Protocol for Cloud-based IoT Services

The existing high-friction device onboarding process hinders the promise and potentiality of Internet of Things (IoT). Even after several attempts by various device manufacturers and working groups, no widely adopted standard solution came to fruition. The latest attempt by Fast Identity Online (FIDO) Alliance promises a zero touch solution for mass market IoT customers, but the burden is transferred to the intermediary supply chain (i.e. they have to maintain infrastructure for managing keys and digital signatures called `Ownership Voucher' for all devices). The specification relies on a `Rendezvous Server' mimicking the notion of Domain Name System (DNS) server'. This essentially means resurrecting all existing possible attack scenarios associated with DNS, which include Denial of Service (DoS) attack, and Correlation attack. `Ownership Voucher' poses the risk that some intermediary supply chain agents may act maliciously and reject the transfer of ownership or sign with a wrong key. Furthermore, the deliberate use of the weak elliptic curve SECP256r1/SECP384r1 (also known as NIST P-256/384) in the specification raises questions. We introduce ASOP: a sovereign and secure device onboarding protocol for IoT devices without blindly trusting the device manufacturer, supply chain, and cloud service provider. The ASOP protocol allows onboarding an IoT device to a cloud server with the help of an authenticator owned by the user. This paper outlines the preliminary development of the protocol and its high-level description. Our `zero-trust' and `human-in-the-loop' approach guarantees that the device owner does not remain at the mercy of third-party infrastructures, and it utilises recently standardized post-quantum cryptographic suite (CRYSTALS) to secure connection and messages.

cs.CR↗

arXiv2024

Expectation Entropy as a Password Strength Metric

The classical combinatorics-based password strength formula provides a result in tens of bits, whereas the NIST Entropy Estimation Suite give a result between 0 and 1 for Min-entropy. In this work, we present a newly developed metric -- Expectation entropy that can be applied to estimate the strength of any random or random-like password. Expectation entropy provides the strength of a password on the same scale as an entropy estimation tool. Having an 'Expectation entropy' of a certain value, for example, 0.4 means that an attacker has to exhaustively search at least 40\% of the total number of guesses to find the password.

cs.CR↗

arXiv2024

A Causal Model for Quantifying Multipartite Classical and Quantum Correlations

We give an operational definition of information-theoretic resources within a given multipartite classical or quantum correlation. We present our causal model that serves as the source coding side of this correlation and introduce a novel concept of resource rate. We argue that, beyond classical secrecy, additional resources exist that are useful for the security of distributed computing problems, which can be captured by the resource rate. Furthermore, we establish a relationship between resource rate and an extension of Shannon's logarithmic information measure, namely, total correlation.

cs.IT↗

arXiv2024

Model CBOR Serialization for Federated Learning

The typical federated learning workflow requires communication between a central server and a large set of clients synchronizing model parameters between each other. The current frameworks use communication protocols not suitable for resource-constrained devices and are either hard to deploy or require high-throughput links not available on these devices. In this paper, we present a generic message framework using CBOR for communication with existing federated learning frameworks optimised for use with resource-constrained devices and low power and lossy network links. We evaluate the resulting message sizes against JSON serialized messages where compare both with model parameters resulting in optimal and worst case serialization length, and with a real-world LeNet-5 model. Our benchmarks show that with our approach, messages are up to 75 % smaller in size when compared to the JSON alternative.

cs.NI↗

arXiv2023

On Gradient-like Explanation under a Black-box Setting: When Black-box Explanations Become as Good as White-box

Attribution methods shed light on the explainability of data-driven approaches such as deep learning models by uncovering the most influential features in a to-be-explained decision. While determining feature attributions via gradients delivers promising results, the internal access required for acquiring gradients can be impractical under safety concerns, thus limiting the applicability of gradient-based approaches. In response to such limited flexibility, this paper presents \methodAbr~(gradient-estimation-based explanation), an approach that produces gradient-like explanations through only query-level access. The proposed approach holds a set of fundamental properties for attribution methods, which are mathematically rigorously proved, ensuring the quality of its explanations. In addition to the theoretical analysis, with a focus on image data, the experimental results empirically demonstrate the superiority of the proposed method over state-of-the-art black-box methods and its competitive performance compared to methods with full access.

cs.LG↗

arXiv2023

Differentially Private Synthetic Data Generation via Lipschitz-Regularised Variational Autoencoders

Synthetic data has been hailed as the silver bullet for privacy preserving data analysis. If a record is not real, then how could it violate a person's privacy? In addition, deep-learning based generative models are employed successfully to approximate complex high-dimensional distributions from data and draw realistic samples from this learned distribution. It is often overlooked though that generative models are prone to memorising many details of individual training records and often generate synthetic data that too closely resembles the underlying sensitive training data, hence violating strong privacy regulations as, e.g., encountered in health care. Differential privacy is the well-known state-of-the-art framework for guaranteeing protection of sensitive individuals' data, allowing aggregate statistics and even machine learning models to be released publicly without compromising privacy. The training mechanisms however often add too much noise during the training process, and thus severely compromise the utility of these private models. Even worse, the tight privacy budgets do not allow for many training epochs so that model quality cannot be properly controlled in practice. In this paper we explore an alternative approach for privately generating data that makes direct use of the inherent stochasticity in generative models, e.g., variational autoencoders. The main idea is to appropriately constrain the continuity modulus of the deep models instead of adding another noise mechanism on top. For this approach, we derive mathematically rigorous privacy guarantees and illustrate its effectiveness with practical experiments.

cs.LG↗

arXiv2023

Explaining text classifiers through progressive neighborhood approximation with realistic samples

The importance of neighborhood construction in local explanation methods has been already highlighted in the literature. And several attempts have been made to improve neighborhood quality for high-dimensional data, for example, texts, by adopting generative models. Although the generators produce more realistic samples, the intuitive sampling approaches in the existing solutions leave the latent space underexplored. To overcome this problem, our work, focusing on local model-agnostic explanations for text classifiers, proposes a progressive approximation approach that refines the neighborhood of a to-be-explained decision with a careful two-stage interpolation using counterfactuals as landmarks. We explicitly specify the two properties that should be satisfied by generative models, the reconstruction ability and the locality-preserving property, to guide the selection of generators for local explanation methods. Moreover, noticing the opacity of generative models during the study, we propose another method that implements progressive neighborhood approximation with probability-based editions as an alternative to the generator-based solution. The explanation results from both methods consist of word-level and instance-level explanations benefiting from the realistic neighborhood. Through exhaustive experiments, we qualitatively and quantitatively demonstrate the effectiveness of the two proposed methods.

cs.CL↗

arXiv2022

Bisparse Blind Deconvolution through Hierarchical Sparse Recovery

The hierarchical sparsity framework, and in particular the HiHTP algorithm, has been successfully applied to many relevant communication engineering problems recently, particularly when the signal space is hierarchically structured. In this paper, the applicability of the HiHTP algorithm for solving the bi-sparse blind deconvolution problem is studied. The bi-sparse blind deconvolution setting here consists of recovering $h$ and $b$ from the knowledge of $h*(Qb)$, where $Q$ is some linear operator, and both $b$ and $h$ are both assumed to be sparse. The approach rests upon lifting the problem to a linear one, and then applying HiHTP, through the \emph{hierarchical sparsity framework}. %In particular, the efficient HiHTP algorithm is proposed for performing the recovery. Then, for a Gaussian draw of the random matrix $Q$, it is theoretically shown that an $s$-sparse $h \in \mathbb{K}^\mu$ and $\sigma$-sparse $b \in \mathbb{K}^n$ with high probability can be recovered when $\mu \succcurlyeq s\log(s)^2\log(\mu)\log(\mu n) + s\sigma \log(n)$.

cs.IT↗

arXiv2022

One-Shot Messaging at Any Load Through Random Sub-Channeling in OFDM

Compressive Sensing has well boosted massive random access protocols over the last decade. In this paper we apply an orthogonal FFT basis as it is used in OFDM, but subdivide its image into so-called sub-channels and let each sub-channel take only a fraction of the load. In a random fashion the subdivision is consecutively applied over a suitable number of time-slots. Within the time-slots the users will not change their sub-channel assignment and send in parallel the data. Activity detection is carried out jointly across time-slots in each of the sub-channels. For such system design we derive three rather fundamental results: i) First, we prove that the subdivision can be driven to the extent that the activity in each sub-channel is sparse by design. An effect that we call sparsity capture effect. ii) Second, we prove that effectively the system can sustain any overload situation relative to the FFT dimension, i.e. detection failure of active and non-active users can be kept below any desired threshold regardless of the number of users. The only price to pay is delay, i.e. the number of time-slots over which cross-detection is performed. We achieve this by jointly exploring the effect of measure concentration in time and frequency and careful system parameter scaling. iii) Third, we prove that parallel to activity detection active users can carry one symbol per pilot resource and time-slot so it supports so-called one-shot messaging. The key to proving these results are new concentration results for sequences of randomly sub-sampled FFTs detecting the sparse vectors "en bloc". Eventually, we show by simulations that the system is scalable resulting in a coarsely 20-fold capacity increase compared to standard OFDM.

cs.IT↗

arXiv2022

Power of Explanations: Towards automatic debiasing in hate speech detection

Hate speech detection is a common downstream application of natural language processing (NLP) in the real world. In spite of the increasing accuracy, current data-driven approaches could easily learn biases from the imbalanced data distributions originating from humans. The deployment of biased models could further enhance the existing social biases. But unlike handling tabular data, defining and mitigating biases in text classifiers, which deal with unstructured data, are more challenging. A popular solution for improving machine learning fairness in NLP is to conduct the debiasing process with a list of potentially discriminated words given by human annotators. In addition to suffering from the risks of overlooking the biased terms, exhaustively identifying bias with human annotators are unsustainable since discrimination is variable among different datasets and may evolve over time. To this end, we propose an automatic misuse detector (MiD) relying on an explanation method for detecting potential bias. And built upon that, an end-to-end debiasing framework with the proposed staged correction is designed for text classifiers without any external resources required.

cs.CL↗

arXiv2022

An Algorithm for Exact Numerical Age-of-Information Evaluation in Multi-Agent Systems

We present an algorithm for the numerical evaluation of the state-space distribution of an Age-of-Information network. Given enough computational resources, the evaluation can be performed to an arbitrary high precision. An Age-of-Information network is described by a vector of natural numbers, that track how outdated status information from various agents is. Our algorithm yields the means to determine any moment of the corresponding stochastic process. This can be extremely valuable for cases in which the network consists of controllers that communicate with one another, as it potentially allows for less conservative control behavior. It also enables the comparison of different policies regarding their performance (minimizing the average Age-of-Information) to a much more accurate degree than was possible before. This is illustrated using the conventional MaxWeight policy and the optimal policy. We also validate and compare the algorithm with Monte-Carlo-Simulations.

eess.SY↗

arXiv2022

Open or not open: Are conventional radio access networks more secure and trustworthy than Open-RAN?

The Open RAN architecture is a promising and future-oriented architecture. It is intended to open up the radio access network (RAN) and enable more innovation and competition in the market. This will lead to RANs for current 5G networks, but especially for future 6G networks, evolving from the current highly integrated, vendor-specific RAN architecture towards disaggregated architectures with open interfaces that will enable to better tailor RAN solutions to the requirements of 5G and 6G applications. However, the introduction of such an open architecture substantially broadens the attack possibilities when compared to conventional RANs. In the past, this has often led to negative headlines that in summary have associated Open RAN with faulty or inadequate security. In this paper, we analyze what components are involved in an Open RAN deployment, how to assess the current state of security, and what measures need to be taken to ensure secure operation.

cs.CR↗

arXiv2021

A Reverse Jensen Inequality Result with Application to Mutual Information Estimation

The Jensen inequality is a widely used tool in a multitude of fields, such as for example information theory and machine learning. It can be also used to derive other standard inequalities such as the inequality of arithmetic and geometric means or the H\"older inequality. In a probabilistic setting, the Jensen inequality describes the relationship between a convex function and the expected value. In this work, we want to look at the probabilistic setting from the reverse direction of the inequality. We show that under minimal constraints and with a proper scaling, the Jensen inequality can be reversed. We believe that the resulting tool can be helpful for many applications and provide a variational estimation of mutual information, where the reverse inequality leads to a new estimator with superior training behavior compared to current estimators.

cs.IT↗

arXiv2021

Guaranteed blind deconvolution and demixing via hierarchically sparse reconstruction

The blind deconvolution problem amounts to reconstructing both a signal and a filter from the convolution of these two. It constitutes a prominent topic in mathematical and engineering literature. In this work, we analyze a sparse version of the problem: The filter $h\in \mathbb{R}^\mu$ is assumed to be $s$-sparse, and the signal $b \in \mathbb{R}^n$ is taken to be $\sigma$-sparse, both supports being unknown. We observe a convolution between the filter and a linear transformation of the signal. Motivated by practically important multi-user communication applications, we derive a recovery guarantee for the simultaneous demixing and deconvolution setting. We achieve efficient recovery by relaxing the problem to a hierarchical sparse recovery for which we can build on a flexible framework. At the same time, for this we pay the price of some sub-optimal guarantees compared to the number of free parameters of the problem. The signal model we consider is sufficiently general to capture many applications in a number of engineering fields. Despite their practical importance, we provide first rigorous performance guarantees for efficient and simple algorithms for the bi-sparse and generalized demixing setting. We complement our analytical results by presenting results of numerical simulations. We find evidence that the sub-optimal scaling $s^2\sigma \log(\mu)\log(n)$ of our derived sufficient condition is likely overly pessimistic and that the observed performance is better described by a scaling proportional to $ s\sigma$ up to log-factors.

cs.IT↗

arXiv2021

Explicit CSI Feedback Compression via Learned Approximate Message Passing

Explicit channel state information at the transmitter side is helpful to improve downlink precoding performance for multi-user MIMO systems. In order to reduce feedback signalling overhead, compression of Channel State Information (CSI) is essential. In this work different low complexity compressed sensing algorithms are compared in the context of an explicit CSI feedback scheme for 5G new radio. A neural network approach, based on learned approximate message passing for the computation of row-sparse solutions to matrix-valued compressed sensing problems is introduced. Due to extensive weight sharing, it shares the low memory footprint and fast evaluation of the forward pass with few iterations of a first order iterative algorithm. Furthermore it can be trained on purely synthetic data prior to deployment. Its performance in the explicit CSI feedback application is evaluated, and its key benefits in terms of computational complexity savings are discussed.

eess.SP↗

arXiv2021

Reinforce Security: A Model-Free Approach Towards Secure Wiretap Coding

The use of deep learning-based techniques for approximating secure encoding functions has attracted considerable interest in wireless communications due to impressive results obtained for general coding and decoding tasks for wireless communication systems. Of particular importance is the development of model-free techniques that work without knowledge about the underlying channel. Such techniques utilize for example generative adversarial networks to estimate and model the conditional channel distribution, mutual information estimation as a reward function, or reinforcement learning. In this paper, the approach of reinforcement learning is studied and, in particular, the policy gradient method for a model-free approach of neural network-based secure encoding is investigated. Previously developed techniques for enforcing a certain co-set structure on the encoding process can be combined with recent reinforcement learning approaches. This new approach is evaluated by extensive simulations, and it is demonstrated that the resulting decoding performance of an eavesdropper is capped at a certain error level.

cs.IT↗

arXiv2021

Measure Concentration on the OFDM-based Random Access Channel

It is well known that CS can boost massive random access protocols. Usually, the protocols operate in some overloaded regime where the sparsity can be exploited. In this paper, we consider a different approach by taking an orthogonal FFT base, subdivide its image into appropriate sub-channels and let each subchannel take only a fraction of the load. To show that this approach can actually achieve the full capacity we provide i) new concentration inequalities, and ii) devise a sparsity capture effect, i.e where the sub-division can be driven such that the activity in each each sub-channel is sparse by design. We show by simulations that the system is scalable resulting in a coarsely 30-fold capacity increase.

cs.IT↗