Integrated sensing and communication systems face critical challenges, including limited bandwidth, power constraints, and varying communication conditions, which demand efficient data transmission and processing strategies. This paper introduces, ByteTrans, a novel joint optimization framework that integrates byte-level predictive modeling with adaptive model scheduling to maximize data transmission efficiency while adhering to communication and computational constraints. The proposed framework employs Transformer-based models to predict and compress data packets losslessly, leveraging the inherent redundancy in multi-modal network data. Such a unified data compression approach predicts occurring byte probabilities, encodes them as ranks using lossless entropy coding, and efficiently reduces data size and entropy across diverse modalities. Then, a dynamic adaptation strategy selects the optimal compression model based on packet characteristics and channel conditions, ensuring efficient operation across heterogeneous sensor environments. Experimental results validate that our scheme achieves compression rates exceeding 50%, while showcasing substantial reductions in communication time and bandwidth usage under both normal and adverse channel conditions. Furthermore, we effectively implement these models across various real-world edge sensors and servers, showcasing their practicality and efficiency in various network applications. By addressing the trade-offs between achieving lower compression ratios and limiting computational and energy consumption, this work establishes a scalable and robust solution for data management in multi-modal communication systems.

Operating a quantum network incurs high capital and operational expenditures, which are expected to be compensated by the high value of enabled quantum applications. However, existing mechanisms mainly focus on maximizing the entanglement distribution rate and neglect the cost incurred on users. This paper aims to address how to utilize quantum network resources in a cost-efficient manner while sustaining high-quantity and high-quality entanglement distribution. We first consider how to establish a steady stream of entanglements between remote nodes with the minimum cost. Utilizing a recent flow-based abstraction and a novel graph representation, we design an optimal algorithm for min-cost remote entanglement distribution. Next, we consider distributing entanglements with the highest fidelity subject to a cost bound and prove its NP-hardness. To explore the cost-fidelity trade-off due to swapping and purification, we propose an approximation scheme for maximizing fidelity while satisfying an arbitrary cost bound. Our algorithms provide rigorous tools for supporting high-performance quantum network applications with financial consideration and offer strong theoretical guarantees. Extensive simulation results validate the advantageous performance in cost efficiency and/or fidelity compared to existing solutions and heuristics.

Low Earth Orbit Satellite Networks (LSNs), as the new generation of backbone networks, can provide low-latency network connectivity anywhere on Earth. However, their dynamic topology and unpredictable global usage patterns hinder reliable communication, limiting their application in supporting real-time applications that require predictable performance. Specifically, the highly dynamic LSN may experience congestion and energy depletion due to uneven user demands and the periodic movement of satellites. In this paper, we design a Congestion and Energy-Aware pricing and resource Reservation algorithm, CEAR, which enables a LSN to reserve network resources for online arriving real-time communication requests, ensuring reliable communication to support performance-critical applications such as disaster monitoring and remote teleconferencing. To maintain the long-term performance of the network, the LSN operator sets resource prices for link bandwidth and satellite energy consumption across the network. The resource prices act as a proxy between the resource reservation decisions for each communication request and the operator’s objective to maximize throughput and network utility and/or to balance network-wide resource depletion. CEAR is guided by online competitive algorithm design and achieves a competitive social welfare. Extensive simulations using real-world LSN topology show that CEAR achieves high social welfare while maintaining low network-wide congestion and energy deficit.

The rapid growth in low-Earth-orbit satellites enables providing Earth observation applications to public users via a shared platform. However, the limited satellite-ground communication resources present a major challenge in downloading and fully utilizing satellite-captured Earth observation data on the ground. As a new edge computing paradigm, orbital edge computing allows satellites to host deep learning models with on-board computing resources for in-orbit data analysis, reducing downlink data volume and response time. However, the limited generalizability of in-orbit models and data distribution shifts across geographical locations severely impact the accuracy of in-orbit analytics. In this work, we design a framework, AdaOrb, which dynamically schedules online model retraining for location-specific Earth observation tasks. Scheduling decisions are made with a model predictive control-based algorithm that allocates limited satellite downlink capacity among onboard tasks to download model retraining data. By developing and using a hardware-in-the-loop orbital edge computing testbed, we show that our method achieves superior overall accuracy of in-orbit analytics tasks compared to alternative methods.

The rapid increase in networked systems and data transmission requires advanced data compression solutions to optimize bandwidth utilization and enhance network performance. This study introduces a novel byte-level predictive model using Transformer architecture, capable of handling the redundancy and diversity of data types in network traffic as byte sequences. Unlike traditional methods that require separate compressors for different data types, this unified approach sets new benchmarks and simplifies predictive modeling across various data modalities such as video, audio, images, and text, by processing them at the byte level. This is achieved by predicting subsequent byte probability distributions, encoding them into a sparse rank sequence using lossless entropy coding, and significantly reducing both data size and entropy. Experimental results1show that our model achieves compression ratios below 50%, while offering models of various sizes tailored for different communication devices. Additionally, we successfully deploy these models on a range of edge devices and servers, demonstrating their practical applicability and effectiveness in real-world network scenarios. This approach significantly enhances data throughput and reduces bandwidth demands, making it particularly valuable in resource-constrained environments like the Internet of Things sensor networks.

Edge computing gained popularity for its promises of low latency and high-quality computing services to users. However, it has also introduced the challenge of mutual untrust between user and edge devices for service level agreement (SLA) compliance. This obstacle hampers wide adoption of edge computing, especially in pervasive edge computing (PEC) where edge devices can freely enter or exit the market, which makes verifying and enforcing SLAs significantly more challenging. In this paper, we propose a framework for verifying and enforcing SLAs in PEC, allowing a user to assess SLA compliance of an edge service and ensure correctness of the service results. Our solution, called VeriEdge, employs a verifiable delayed sampling approach to sample a small number of computation steps, and relies on randomly selected verifiers to verify correctness of the computation results. To make sure the verification process is non-manipulable, we employ verifiable random functions to post-select the verifier(s). A dispute protocol is designed to resolve disputes for potential misbehavior. Rigorous security analysis demonstrates that VeriEdge achieves a high probability of detecting SLA violation with a minimal overhead. Experimental results indicate that VeriEdge is lightweight, practical, and efficient.

Various methods have been proposed for protecting the speaker's identity while preserving speech intelligibility. However, existing studies fail to consider the overall tradeoff between speech utility, speaker verification, and inference of voice physical attributes, such as emotion, age, accent, and gender. we propose a tradeoff metric to encapsulate voice biometrics as well as different voice attributes, to study the feasibility of applying cutting-edge voice anonymization solutions to achieve the optimum tradeoff between privacy protection and speech utility.

A quantum network distributes quantum entanglements between remote nodes, and is key to many applications in secure communication, quantum sensing and distributed quantum computing. This paper explores the fundamental trade-off between the throughput and the quality of entanglement distribution in a multi-hop quantum repeater network. Compared to existing work which aims to heuristically maximize the entanglement distribution rate (EDR) and/or entanglement fidelity, our goal is to characterize the maximum achievable worst-case fidelity, while satisfying a bound on the maximum achievable expected EDR between an arbitrary pair of quantum nodes. This characterization will provide fundamental bounds on the achievable performance region of a quantum network, which can assist with the design of quantum network topology, protocols and applications. However, the task is highly non-trivial and is NP-hard as we shall prove. Our main contribution is a fully polynomial-time approximation scheme to approximate the achievable worst-case fidelity subject to a strict expected EDR bound, combining an optimal fidelity-agnostic EDR-maximizing formulation and a worst-case isotropic noise model. The EDR and fidelity guarantees can be implemented by a post-selection-and-storage protocol with quantum memories. By developing a discrete-time quantum network simulator, we conduct simulations to show the characterized performance region (the approximate Pareto frontier) of a network, and demonstrate that the designed protocol can achieve the performance region while existing protocols exhibit a substantial gap.

Scalability is a critical challenge for blockchain-based cryptocurrencies. Payment channel networks (PCNs) have emerged as a promising solution for this challenge. However, channel balance depletion can significantly limit the capacity and usability of a PCN. Specifically, frequent transactions that result in unbalanced payment flows from two ends of a channel can quickly deplete the balance on one end, thus blocking future payments from that direction. In this paper, we propose Fence, an online balance-aware fee setting algorithm to prevent channel depletion and improve PCN sustainability and long-term throughput. In our algorithm, PCN routers set transaction fees based on the current balance and level of congestion on each channel, in order to incentivize payment senders to utilize paths with more balance and less congestion. Our algorithm is guided by online competitive algorithm design, and achieves an asymptotically tight competitive ratio with constant violation in a unidirectional PCN. We further prove that no online algorithm can achieve a finite competitive ratio in a general PCN. Extensive simulations under a real-world PCN topology show that Fence achieves high throughput and keeps network channels balanced, compared to state-of-the-art PCN routing algorithms.

Quantum entanglement distribution between remote nodes is key to many promising quantum applications. Existing mechanisms have mainly focused on improving throughput and fidelity via entanglement routing or single-node scheduling. This paper considers entanglement scheduling and distribution among many source-destination pairs with different requests over an entire quantum network topology. Two practical scenarios are considered. When requests do not have deadlines, we seek to minimize the average completion time of the communication requests. If deadlines are specified, we seek to maximize the number of requests whose deadlines are met. Inspired by optimal scheduling disciplines in conventional single-queue scenarios, we design a general optimization framework for entanglement scheduling and distribution called ESDI, and develop a probabilistic protocol to implement the optimized solutions in a general buffered quantum network. We develop a discrete-time quantum network simulator for evaluation. Results show the superior performance of ESDI compared to existing solutions.

The wide spread of vehicular cameras has raised broad privacy concerns. Ubiquitous vehicular cameras capture bystanders like people or cars nearby without their awareness. To address privacy concerns, most existing works either blur out direct identifiers such as vehicle license plates and human faces, or obfuscate whole video frames. However, the former solution is vulnerable to re-identification attacks based on general features, and the latter severely impacts utility of the transformed videos. In this paper, we propose an INStance-level PrIvacy-pREserving (INSPIRE) video transformation framework for vehicular camera videos. INSPIRE leverages deep neural network models to detect and replace sensitive object instances in vehicular videos with their non-existent counterparts. We design INSPIRE as a modular framework to enable flexible customization of protected instance categories and their protection modules. An implementation of INSPIRE focused on protecting people and cars is described, which we tested on six re-identification datasets and three real-world vehicular video datasets to evaluate its privacy protection and utility preservation capability. Results show that INSPIRE can thwart 97% of re-identification attacks for people and cars while maintaining a 0.75 object detection mean average precision on transformed instances. We also demonstrate experimentally that INSPIRE is robust against model inversion attacks. Compared to solutions that provide comparable privacy protection, INSPIRE achieves relatively 1.76 times higher counting accuracy and 31.61% higher object detection mean average precision.

Many people are concerned about how their personal data is used for online behavioral advertising (OBA). Ad targeting explanations have been proposed as a way to reduce this concern by improving transparency. However, it is unclear when and why people might want ad targeting explanations. Without this insight, we run the risk of designing explanations that do not address real concerns. To bridge this gap, we conducted a four-week, mixed-methods field study with 60 participants to understand when and why people want targeting explanations for the ads they actually encountered while browsing the web. We found that users wanted explanations for around 30% of the 4,251 ads we asked them about during the study, and that subjective perceptions of how their personal data was collected and shared were highly correlated with when users wanted ad explanations. Often, users wanted these explanations to confirm or deny their own preconceptions about how their data was collected or the motives of advertisers. A key upshot of our work is that one-size-fits-all approaches to ad explanations are likely to fail at addressing people’s lived concerns about ad targeting; instead, more personalized explanations are needed.

Voice-based human-computer interaction has become pervasive in laptops, smartphones, home voice assistants, and Internet of Thing (IoT) devices. However, voice interaction comes with security and privacy risks. Numerous privacy-preserving measures have been proposed for hiding the speaker's identity while maintaining speech intelligibility. However, existing works do not consider the overall tradeoff between speech utility, speaker verification, and inference of voice attributes, including emotional state, age, accent, and gender. In this study, we first develop a tradeoff metric to capture voice biometrics as well as different voice attributes. We then propose VoicePM, a robust Voice Privacy Measurement framework, to study the feasibility of applying different state-of-the-art voice anonymization solutions to achieve the optimum tradeoff between privacy and utility. We conduct extensive experiments using anonymization approaches covering signal processing, voice synthesis, voice conversion, and adversarial techniques on three speech datasets that include both English and Chinese speakers to showcase the effectiveness and feasibility of VoicePM.

This paper studies how an edge-based federated learning algorithm called FedAegis can be designed to be ro-bust under both heterogeneous data distributions and Byzantine adversaries. The divergence of local data distributions leads to suboptimal results for the training process of federated learning, and the Byzantine adversaries aim to prevent the training process from converging in a distributed learning system. In this paper, we show that an edge-based hierarchical federated learning architecture can help tackle this dilemma by utilizing edge nodes geographically close to clusters of local devices. By combining a distributionally robust global loss function with a local Byzantine-robust aggregation rule, FedAegis can defend against remote Byzantine adversaries who cannot manipulate local devices' connections to edge nodes, meanwhile accounting for global data heterogeneity across benign local devices. Experiments with the MNIST, FMNIST and CIFAR-IO datasets show that our proposed algorithm can achieve convergence and high accuracy under heterogeneous data and various attack scenarios, while state-of-the-art defenses and robustness mechanisms are non-converging or have reduced average and/or worst-case accuracy.

Payment Channel Network (PCN) is an auspicious solution to the scalability issue of the blockchain, improving transaction throughput without relying on on-chain transactions. In a PCN, nodes can set prices for forwarding payments on behalf of other nodes, which motivates participation and improves network stability. Analyzing the price setting behaviors of PCN nodes plays a key role in understanding the economic properties of PCNs, but has been under-studied in the literature. In this paper, we apply equilibrium analysis to the price-setting game between two payment hubs in the PCN with limited channel capacities and partial overlap demand. We analyze existence of pure Nash Equilibriums (NEs) and bounds on the equilibrium revenue under various cases, and propose an algorithm to find all pure NEs. Using real data, we show bounds on the price of anarchy/stability and average transaction fee under realistic network conditions, and draw conclusions on the economic advantage of the PCN for making payment transfers by cryptocurrency users.

Centralized online social networks --- e.g., Facebook, Twitter and TikTok --- help drive social connection on the Internet, but have nigh unfettered access to monitor and monetize the personal data of their users. This centralization can especially undermine the use of the social internet by minority populations, who disproportionately bear the costs of institutional surveillance. We introduce a new class of privacy-enhancing technology --- decentralized privacy overlays (DePOs) --- that helps cOSN users regain some control over their personal data by allowing them to selectively share secret content on cOSNs through decentralized content distribution networks. As a first step, we present an implementation and user evaluation of Image DePO, a proof-of-concept design probe that allows users to upload and share secret photos on Facebook through the Interplanetary File System peer-to-peer protocol. We qualitatively evaluated Image DePO in a controlled, test environment with 19 queer and Black, Indigenous, (and) Person of Color (BIPOC) participants. We found that while Image DePO could help address the institutional threats with which our participants expressed concern, interpersonal threats were the more salient concern in their decisions to share content. Accordingly, we argue that in order to see widespread use, DePOs must align protection against abstract institutional threats with protection against the more salient interpersonal threats users consider when making specific sharing decisions.

Bluetooth requires device pairing to ensure security in data transmission, encumbering a number of ad-hoc, transactional interactions that require both ease-of-use and “good enough” security: e.g., sharing contact information or secure links to people nearby. We introduce Bit Whisperer, an ad-hoc short-range wireless communication system that enables “walk up and share” data transmissions with “good enough” security. Bit Whisperer transmits data to proximate devices co-located on a solid surface through high frequency, inaudible acoustic signals. The physical surface has two benefits: it enhances acoustic signal transmission by reflecting sound waves as they propagate; and, it makes the domain of communication visible, helping users identify exactly with whom they are sharing data without prior pairing. Through a series of technical evaluations, we demonstrate that Bit Whisperer is robust for common use-cases and secure against likely threats. We also implement three example applications to demonstrate the utility of Whisperer: 1-to-1 local contact sharing, 1-to-N private link sharing to open a secure group chat, and 1-to-N local device authentication.