arXiv subjects

Alejandro Cohen

Publications and source records attributed to Alejandro Cohen.

At least 37 records · Page 2Linked to original sources

arXiv2024

Error Correction Capabilities of Non-Linear Cryptographic Hash Functions

Linear hashes are known to possess error-correcting capabilities. However, in most applications, non-linear hashes with pseudorandom outputs are utilized instead. It has also been established that classical non-systematic random codes, both linear and non-linear, are capacity achieving in the asymptotic regime. Thus, it is reasonable to expect that non-linear hashes might also exhibit good error-correcting capabilities. In this paper, we show this to be the case. Our proof is based on techniques from multiple access channels. As a consequence, we show that Systematic Random Non-Linear Codes (S-RNLC) are capacity achieving in the asymptotic regime. We validate our results by comparing the performance of the Secure Hash Algorithm (SHA) with that of Systematic Random Linear Codes (SRLC) and S-RNLC, demonstrating that SHA performs equally.

cs.IT↗

arXiv2024

Time Encoding Quantization of Bandlimited and Finite-Rate-of-Innovation Signals

This paper studies the impact of quantization in integrate-and-fire time encoding machine (IF-TEM) sampler used for bandlimited (BL) and finite-rate-of-innovation (FRI) signals. An upper bound is derived for the mean squared error (MSE) of IF-TEM sampler and is compared against that of classical analog-to-digital converters (ADCs) with uniform sampling and quantization. The interplay between a signal's energy, bandwidth, and peak amplitude is used to identify how the MSE of IF-TEM sampler with quantization is influenced by these parameters. More precisely, the quantization step size of the IF-TEM sampler can be reduced when the maximum frequency of a bandlimited signal or the number of pulses of an FRI signal is increased. Leveraging this insight, specific parameter settings are identified for which the quantized IF-TEM sampler achieves an MSE bound that is roughly 8 dB lower than that of a classical ADC with the same number of bits. Experimental results validate the theoretical conclusions.

cs.IT↗

arXiv2024

On the Benefits of Coding for Network Slicing

Network slicing has emerged as an integral concept in 5G, aiming to partition the physical network infrastructure into isolated slices, customized for specific applications. We theoretically formulate the key performance metrics of an application, in terms of goodput and delivery delay, at a cost of network resources in terms of bandwidth. We explore an un-coded communication protocol that uses feedback-based repetitions, and a coded protocol, implementing random linear network coding and using coding-aware acknowledgments. We find that coding reduces the resource demands of a slice to meet the requirements for an application, thereby serving more applications efficiently. Coded slices thus free up resources for other slices, be they coded or not. Based on these results, we propose a hybrid approach, wherein coding is introduced selectively in certain network slices. This approach not only facilitates a smoother transition from un-coded systems to coded systems but also reduces costs across all slices. Theoretical findings in this paper are validated and expanded upon through real-time simulations of the network.

cs.NI↗

arXiv2024

Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates

Synchronous federated learning (FL) is a popular paradigm for collaborative edge learning. It typically involves a set of heterogeneous devices locally training neural network (NN) models in parallel with periodic centralized aggregations. As some of the devices may have limited computational resources and varying availability, FL latency is highly sensitive to stragglers. Conventional approaches discard incomplete intra-model updates done by stragglers, alter the amount of local workload and architecture, or resort to asynchronous settings; which all affect the trained model performance under tight training latency constraints. In this work, we propose straggler-aware layer-wise federated learning (SALF) that leverages the optimization procedure of NNs via backpropagation to update the global model in a layer-wise fashion. SALF allows stragglers to synchronously convey partial gradients, having each layer of the global model be updated independently with a different contributing set of users. We provide a theoretical analysis, establishing convergence guarantees for the global model under mild assumptions on the distribution of the participating devices, revealing that SALF converges at the same asymptotic rate as FL with no timing limitations. This insight is matched with empirical observations, demonstrating the performance gains of SALF compared to alternative mechanisms mitigating the device heterogeneity gap in FL.

cs.LG↗

arXiv2024

Coding-Based Hybrid Post-Quantum Cryptosystem for Non-Uniform Information

We introduce for non-uniform messages a novel hybrid universal network coding cryptosystem (NU-HUNCC) in the finite blocklength regime that provides Post-Quantum (PQ) security at high communication rates. Recently, hybrid cryptosystems offered PQ security by premixing the data using secure coding schemes and encrypting only a small portion of it, assuming the data is uniformly distributed. An assumption that is often challenging to enforce. Standard fixed-length lossless source coding and compression schemes guarantee a uniform output in normalized divergence. Yet, his is not sufficient to guarantee security. We consider an efficient almost uniform compression scheme in non-normalized variational distance for the proposed hybrid cryptosystem, that by utilizing uniform sub-linear shared seed, guarantees PQ security. Specifically, for the proposed PQ cryptosystem, first, we provide an end-to-end coding scheme, NU-HUNCC, for non-uniform messages. Second, we show that NU-HUNCC is information-theoretic individually secured (IS) against an eavesdropper with access to any subset of the links. Third, we introduce a modified security definition, individually semantically secure under a chosen ciphertext attack (ISS-CCA1), and show that against an all-observing eavesdropper, NU-HUNCC satisfies its conditions. Finally, we provide an analysis that shows the high communication rate of NU-HUNCC and the negligibility of the shared seed size.

cs.CR↗

arXiv2024

Successive Refinement in Large-Scale Computation: Advancing Model Inference Applications

Modern computationally-intensive applications often operate under time constraints, necessitating acceleration methods and distribution of computational workloads across multiple entities. However, the outcome is either achieved within the desired timeline or not, and in the latter case, valuable resources are wasted. In this paper, we introduce solutions for layered-resolution computation. These solutions allow lower-resolution results to be obtained at an earlier stage than the final result. This innovation notably enhances the deadline-based systems, as if a computational job is terminated due to time constraints, an approximate version of the final result can still be generated. Moreover, in certain operational regimes, a high-resolution result might be unnecessary, because the low-resolution result may already deviate significantly from the decision threshold, for example in AI-based decision-making systems. Therefore, operators can decide whether higher resolution is needed or not based on intermediate results, enabling computations with adaptive resolution. We present our framework for two critical and computationally demanding jobs: distributed matrix multiplication (linear) and model inference in machine learning (nonlinear). Our theoretical and empirical results demonstrate that the execution delay for the first resolution is significantly shorter than that for the final resolution, while maintaining overall complexity comparable to the conventional one-shot approach. Our experiments further illustrate how the layering feature increases the likelihood of meeting deadlines and enables adaptability and transparency in massive, large-scale computations.

cs.IT↗

arXiv2023

CRYPTO-MINE: Cryptanalysis via Mutual Information Neural Estimation

The use of Mutual Information (MI) as a measure to evaluate the efficiency of cryptosystems has an extensive history. However, estimating MI between unknown random variables in a high-dimensional space is challenging. Recent advances in machine learning have enabled progress in estimating MI using neural networks. This work presents a novel application of MI estimation in the field of cryptography. We propose applying this methodology directly to estimate the MI between plaintext and ciphertext in a chosen plaintext attack. The leaked information, if any, from the encryption could potentially be exploited by adversaries to compromise the computational security of the cryptosystem. We evaluate the efficiency of our approach by empirically analyzing multiple encryption schemes and baseline approaches. Furthermore, we extend the analysis to novel network coding-based cryptosystems that provide individual secrecy and study the relationship between information leakage and input distribution.

cs.CR↗

arXiv2023

CERMET: Coding for Energy Reduction with Multiple Encryption Techniques -- $It's\ easy\ being\ green$

This paper presents CERMET, an energy-efficient hardware architecture designed for hardware-constrained cryptosystems. CERMET employs a base cryptosystem in conjunction with network coding to provide both information-theoretic and computational security while reducing energy consumption per bit. This paper introduces the hardware architecture for the system and explores various optimizations to enhance its performance. The universality of the approach is demonstrated by designing the architecture to accommodate both asymmetric and symmetric cryptosystems. The analysis reveals that the benefits of this proposed approach are multifold, reducing energy per bit and area without compromising security or throughput. The optimized hardware architectures can achieve below 1 pJ/bit operations for AES-256. Furthermore, for a public key cryptosystem based on Elliptic Curve Cryptography (ECC), a remarkable 14.6X reduction in energy per bit and a 9.3X reduction in area are observed, bringing it to less than 1 nJ/bit.

cs.CR↗

arXiv2022

Compressed IF-TEM: Time Encoding Analog-To-Digital Compression

An integrate-and-fire time-encoding-machine (IF-TEM) is an energy-efficient asynchronous sampler. Utilizing the IF-TEM sampler for bandlimited signals, we introduce designs for time encoding and decoding with analog compression prior to the quantization phase. Before the quantizer, efficient analog compression is conducted based on the stationarity of the encoded signal, which is a fundamental characteristic of IF-TEM processing. Low-bit-rate reconstruction is achieved by subdividing the known IF-TEM dynamic range into tighter windows, which can be either fixed size or dynamically changed, and detecting in which window the sample resides. We demonstrate empirically that employing the same number of samples and up to 7% additional bits than the conventional IF-TEM results in a 5-20dB improvement in MSE. Fixing the reconstruction MSE target and the number of samples, using the compressed IF-TEM enables the use of 1-2 fewer bits compared to the classical IF-TEM.

cs.IT↗

arXiv2022

Ultra-Reliable Low-Latency Millimeter-Wave Communications with Sliding Window Network Coding

Ultra-reliability and low-latency are pivotal requirements of the new 6th generation of communication systems (xURLLC). Over the past years, to increase throughput, adaptive active antennas were introduced in advanced wireless communications, specifically in the domain of millimeter-wave (mmWave). Consequently, new lower-layer techniques were proposed to cope with practical challenges of high dimensional and electronically-steerable beams. The transition from omni-directional to highly directional antennas presents a new type of wireless systems that deliver high bandwidth, but that are susceptible to high losses and high latency variation. Classical approaches cannot close the rising gap between high throughput and low delay in those advanced systems. In this work, we incorporate effective sliding window network coding solutions in mmWave communications. While legacy systems such as rateless codes improve delay, cross-layer results show that they do not provide low latency communications (LLC - below 10 ms), due to the lossy behaviour of mmWave channel and the lower-layers' retransmission mechanisms. On the other hand, fixed sliding window random linear network coding (RLNC) is able to achieve LLC, and even better, adaptive sliding window RLNC obtains ultra-reliable LLC (Ultra-Reliable and Low-Latency Communications (URLLC) - LLC with maximum delay below 10 ms with more than 99% success rate).

cs.IT↗

arXiv2022

AES as Error Correction: Cryptosystems for Reliable Communication

In this paper, we show that the Advanced Encryption Standard (AES) cryptosystem can be used as an error-correcting code to obtain reliability over noisy communication and data systems. Moreover, we characterize a family of computational cryptosystems that can potentially be used as well performing error correcting codes. In particular, we show that simple padding followed by a cryptosystem with uniform or pseudo-uniform outputs can approach the error-correcting performance of random codes. We empirically contrast the performance of the proposed approach using AES as error correction with that of Random Linear Codes and CA-Polar codes and show that in practical scenarios, they achieve almost the same performance. Finally, we present a modified counter mode of operation, named input plaintext counter mode, in order to utilize AES for multiple blocks while retaining its error correcting capabilities.

cs.IT↗

arXiv2022

Multi-Level Group Testing with Application to One-Shot Pooled COVID-19 Tests

A key requirement in containing contagious diseases, such as the Coronavirus disease 2019 (COVID-19) pandemic, is the ability to efficiently carry out mass diagnosis over large populations. Some of the leading testing procedures, such as those utilizing qualitative polymerase chain reaction, involve using dedicated machinery which can simultaneously process a limited amount of samples. A candidate method to increase the test throughput is to examine pooled samples comprised of a mixture of samples from different patients. In this work we study pooling based tests which operate in a one-shot fashion, while providing an indication not solely on the presence of infection, but also on its level, without additional pool tests, as often required in COVID-19 testing. As these requirements limit the application of traditional group-testing (GT) methods, we propose a multi-level GT scheme, which builds upon GT principles to enable accurate recovery using much fewer tests than patients, while operating in a one-shot manner and providing multi-level indications. We provide a theoretical analysis of the proposed scheme and characterize conditions under which the algorithm operates reliably and at affordable computational complexity. Our numerical results demonstrate that multi level GT accurately and efficiently detects infection levels, while achieving improved performance over previously proposed one-shot COVID-19 pooled-testing methods.

q-bio.QM↗

arXiv2022

FlEC: Enhancing QUIC with application-tailored reliability mechanisms

Packet losses are common events in today's networks. They usually result in longer delivery times for application data since retransmissions are the de facto technique to recover from such losses. Retransmissions is a good strategy for many applications but it may lead to poor performance with latency-sensitive applications compared to network coding. Although different types of network coding techniques have been proposed to reduce the impact of losses by transmitting redundant information, they are not widely used. Some niche applications include their own variant of Forward Erasure Correction (FEC) techniques, but there is no generic protocol that enables many applications to easily use them. We close this gap by designing, implementing and evaluating a new Flexible Erasure Correction (FlEC) framework inside the newly standardized QUIC protocol. With FlEC, an application can easily select the reliability mechanism that meets its requirements, from pure retransmissions to various forms of FEC. We consider three different use cases: $(i)$ bulk data transfer, $(ii)$ file transfers with restricted buffers and $(iii)$ delay-constrained messages. We demonstrate that modern transport protocols such as QUIC may benefit from application knowledge by leveraging this knowledge in FlEC to provide better loss recovery and stream scheduling. Our evaluation over a wide range of scenarios shows that the FlEC framework outperforms the standard QUIC reliability mechanisms from a latency viewpoint.

cs.IT↗

arXiv2022

Absolute Security in High-Frequency Wireless Links

Security against eavesdropping is one of the key concerns in the design of any communication system. Many common considerations of the security of a wireless communication channel rely on comparing the signal level measured by Bob (the intended receiver) to that accessible to Eve (an eavesdropper). Frameworks such as Wyner's wiretap model ensure the security of a link, in an average sense, when Bob's signal-to-noise ratio exceeds Eve's. Unfortunately, because these guarantees rely on statistical assumptions about noise, Eve can still occasionally succeed in decoding information. The goal of achieving exactly zero probability of intercept over an engineered region of the broadcast sector, which we term absolute security, remains elusive. Here, we describe the first architecture for a wireless link which provides absolute security. Our approach relies on the inherent properties of broadband and high-gain antennas, and is therefore ideally suited for implementation in millimeter-wave and terahertz wireless systems, where such antennas will generally be employed. We exploit spatial minima of the antenna pattern at different frequencies, the union of which defines a wide region where Eve is guaranteed to fail regardless of her computational capabilities, and regardless of the noise in the channels. Unlike conventional zero-forcing beam forming methods, we show that, for realistic assumptions about the antenna configuration and power budget, this absolute security guarantee can be achieved over most possible eavesdropper locations. Since we use relatively simple frequency-multiplexed coding, together with the underlying physics of a diffracting aperture, this idea is broadly applicable in many contexts.

cs.IT↗

arXiv2022

Distributed Computations with Layered Resolution

Modern computationally-heavy applications are often time-sensitive, demanding distributed strategies to accelerate them. On the other hand, distributed computing suffers from the bottleneck of slow workers in practice. Distributed coded computing is an attractive solution that adds redundancy such that a subset of distributed computations suffices to obtain the final result. However, the final result is still either obtained within a desired time or not, and for the latter, the resources that are spent are wasted. In this paper, we introduce the novel concept of layered-resolution distributed coded computations such that lower resolutions of the final result are obtained from collective results of the workers -- at an earlier stage than the final result. This innovation makes it possible to have more effective deadline-based systems, since even if a computational job is terminated because of timing, an approximated version of the final result can be released. Based on our theoretical and empirical results, the average execution delay for the first resolution is notably smaller than the one for the final resolution. Moreover, the probability of meeting a deadline is one for the first resolution in a setting where the final resolution exceeds the deadline almost all the time, reducing the success rate of the systems with no layering.

cs.DC↗

arXiv2022

Stream Iterative Distributed Coded Computing for Learning Applications in Heterogeneous Systems

To improve the utility of learning applications and render machine learning solutions feasible for complex applications, a substantial amount of heavy computations is needed. Thus, it is essential to delegate the computations among several workers, which brings up the major challenge of coping with delays and failures caused by the system's heterogeneity and uncertainties. In particular, minimizing the end-to-end job in-order execution delay, from arrival to delivery, is of great importance for real-world delay-sensitive applications. In this paper, for computation of each job iteration in a stochastic heterogeneous distributed system where the workers vary in their computing and communicating powers, we present a novel joint scheduling-coding framework that optimally split the coded computational load among the workers. This closes the gap between the workers' response time, and is critical to maximize the resource utilization. To further reduce the in-order execution delay, we also incorporate redundant computations in each iteration of a distributed computational job. Our simulation results demonstrate that the delay obtained using the proposed solution is dramatically lower than the uniform split which is oblivious to the system's heterogeneity and, in fact, is very close to an ideal lower bound just by introducing a small percentage of redundant computations.

cs.DC↗

arXiv2022

Partial Encryption after Encoding for Security and Reliability in Data Systems

We consider the problem of secure and reliable communication over a noisy multipath network. Previous work considering a noiseless version of our problem proposed a hybrid universal network coding cryptosystem (HUNCC). By combining an information-theoretically secure encoder together with partial encryption, HUNCC is able to obtain security guarantees, even in the presence of an all-observing eavesdropper. In this paper, we propose a version of HUNCC for noisy channels (N-HUNCC). This modification requires four main novelties. First, we present a network coding construction which is jointly, individually secure and error-correcting. Second, we introduce a new security definition which is a computational analogue of individual security, which we call individual indistinguishability under chosen ciphertext attack (individual IND-CCA1), and show that NHUNCC satisfies it. Third, we present a noise based decoder for N-HUNCC, which permits the decoding of the encoded-thenencrypted data. Finally, we discuss how to select parameters for N-HUNCC and its error-correcting capabilities.

cs.IT↗

arXiv2022

Broadcast Approach Meets Network Coding for Data Streaming

For data streaming applications, existing solutions are not yet able to close the gap between high data rates and low delay. This work considers the problem of data streaming under mixed delay constraints over a single communication channel with delayed feedback. We propose a novel layered adaptive causal random linear network coding (LAC-RLNC) approach with forward error correction. LAC-RLNC is a variable-to-variable coding scheme, i.e., variable recovered information data at the receiver over variable short block length and rate is proposed. Specifically, for data streaming with base and enhancement layers of content, we characterize a high dimensional throughput-delay trade-off managed by the adaptive causal layering coding scheme. The base layer is designed to satisfy the strict delay constraints, as it contains the data needed to allow the streaming service. Then, the sender can manage the throughput-delay trade-off of the second layer by adjusting the retransmission rate a priori and posterior as the enhancement layer, that contains the remaining data to augment the streaming service's quality, is with the relax delay constraints. We numerically show that the layered network coding approach can dramatically increase performance. We demonstrate that LAC-RLNC compared with the non-layered approach gains a factor of three in mean and maximum delay for the base layer, close to the lower bound, and factor two for the enhancement layer.

cs.IT↗