Towards Provably Invisible Network Flow Fingerprints

Network traffic analysis reveals important information even when messages are encrypted. We consider active traffic analysis via flow fingerprinting by invisibly embedding information into packet timings of flows. In particular, assume Alice wishes to embed fingerprints into flows of a set of network input links, whose packet timings are modeled by Poisson processes, without being detected by a watchful adversary Willie. Bob, who receives the set of fingerprinted flows after they pass through the network modeled as a collection of independent and parallel $M/M/1$ queues, wishes to extract Alice's embedded fingerprints to infer the connection between input and output links of the network. We consider two scenarios: 1) Alice embeds fingerprints in all of the flows; 2) Alice embeds fingerprints in each flow independently with probability $p$. Assuming that the flow rates are equal, we calculate the maximum number of flows in which Alice can invisibly embed fingerprints while having those fingerprints successfully decoded by Bob. Then, we extend the construction and analysis to the case where flow rates are distinct, and discuss the extension of the network model

Asymptotic Loss in Privacy due to Dependency in Gaussian Traces

The rapid growth of the Internet of Things (IoT) necessitates employing privacy-preserving techniques to protect users' sensitive information. Even when user traces are anonymized, statistical matching can be employed to infer sensitive information. In our previous work, we have established the privacy requirements for the case that the user traces are instantiations of discrete random variables and the adversary knows only the structure of the dependency graph, i.e., whether each pair of users is connected. In this paper, we consider the case where data traces are instantiations of Gaussian random variables and the adversary knows not only the structure of the graph but also the pairwise correlation coefficients. We establish the requirements on anonymization to thwart such statistical matching, which demonstrate the significant degree to which knowledge of the pairwise correlation coefficients further significantly aids the adversary in breaking user anonymity.Comment: IEEE Wireless Communications and Networking Conferenc

Unsupervised Representations Improve Supervised Learning in Speech Emotion Recognition

Speech Emotion Recognition (SER) plays a pivotal role in enhancing human-computer interaction by enabling a deeper understanding of emotional states across a wide range of applications, contributing to more empathetic and effective communication. This study proposes an innovative approach that integrates self-supervised feature extraction with supervised classification for emotion recognition from small audio segments. In the preprocessing step, to eliminate the need of crafting audio features, we employed a self-supervised feature extractor, based on the Wav2Vec model, to capture acoustic features from audio data. Then, the output featuremaps of the preprocessing step are fed to a custom designed Convolutional Neural Network (CNN)-based model to perform emotion classification. Utilizing the ShEMO dataset as our testing ground, the proposed method surpasses two baseline methods, i.e. support vector machine classifier and transfer learning of a pretrained CNN. comparing the propose method to the state-of-the-art methods in SER task indicates the superiority of the proposed method. Our findings underscore the pivotal role of deep unsupervised feature learning in elevating the landscape of SER, offering enhanced emotional comprehension in the realm of human-computer interactions

Improved shear strength performance of compacted rubberized clays treated with sodium alginate biopolymer

This study examines the potential use of sodium alginate (SA) biopolymer as an environmentally sustainable agent for the stabilization of rubberized soil blends prepared using a high plasticity clay soil and tire-derived ground rubber (GR). The experimental program consisted of uniaxial compression and scanning electron microscopy (SEM) tests; the former was performed on three soil–GR blends (with GR-to-soil mass ratios of 0%, 5% and 10%) compacted (and cured for 1, 4, 7 and 14 d) employing distilled water and three SA solutions—prepared at SA-to-water (mass-tovolume) dosage ratios of 5, 10 and 15 g/L—as the compaction liquid. For any given GR content, the greater the SA dosage and/or the longer the curing duration, the higher the uniaxial compressive strength (UCS), with only minor added benefits beyond seven days of curing. This behaviour was attributed to the formation and propagation of so-called “cationic bridges” (developed as a result of a “Ca2+/Mg2

Practical Traffic Analysis Attacks on Secure Messaging Applications

Instant Messaging (IM) applications like Telegram, Signal, and WhatsApp have become extremely popular in recent years. Unfortunately, such IM services have been targets of continuous governmental surveillance and censorship, as these services are home to public and private communication channels on socially and politically sensitive topics. To protect their clients, popular IM services deploy state-of-the-art encryption mechanisms. In this paper, we show that despite the use of advanced encryption, popular IM applications leak sensitive information about their clients to adversaries who merely monitor their encrypted IM traffic, with no need for leveraging any software vulnerabilities of IM applications. Specifically, we devise traffic analysis attacks that enable an adversary to identify administrators as well as members of target IM channels (e.g., forums) with high accuracies. We believe that our study demonstrates a significant, real-world threat to the users of such services given the increasing attempts by oppressive governments at cracking down controversial IM channels. We demonstrate the practicality of our traffic analysis attacks through extensive experiments on real-world IM communications. We show that standard countermeasure techniques such as adding cover traffic can degrade the effectiveness of the attacks we introduce in this paper. We hope that our study will encourage IM providers to integrate effective traffic obfuscation countermeasures into their software. In the meantime, we have designed and deployed an open-source, publicly available countermeasure system, called IMProxy, that can be used by IM clients with no need for any support from IM providers. We have demonstrated the effectiveness of IMProxy through experiments

Limbal Mass as a Presentation of Parotid Gland Undifferentiated Carcinoma: A Case Report

Metastatic neoplasms to the ocular surface are extremely rare. Here, we describe a case of A 56-year-old man developed simultaneously a limbal and parotid gland masses in his left side. He underwent excisional biopsy of limbal mass, parotidectomy, and systemic evaluation. Histopathologically, multislice sections of both limbal and parotidal masses disclosed an undifferentiated carcinoma of both sites. Further evaluation revealed no other site of involvement and metastasis. The patient underwent systemic chemotherapy and local radiotherapy for parotid gland tumor. Distal metastasis from undifferentiated carcinoma of the parotid gland to ocular surface is very rare and to the best of our knowledge has not been previously reported. This is the first report of the manifestation of metastasis from undifferentiated carcinoma of parotid gland origin to the limbus. The limbal mass may be the initial manifestation of metastasis from this origin and should be considered in the differential diagnosis of a metastatic limbal tumor