@article{wang_yu_yang_xue_gu_li_zhou_2023, title={Fence: Fee-Based Online Balance-Aware Routing in Payment Channel Networks}, volume={10}, ISSN={["1558-2566"]}, DOI={10.1109/TNET.2023.3324136}, abstractNote={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.}, journal={IEEE-ACM TRANSACTIONS ON NETWORKING}, author={Wang, Xiaojian and Yu, Ruozhou and Yang, Dejun and Xue, Guoliang and Gu, Huayue and Li, Zhouyu and Zhou, Fangtong}, year={2023}, month={Oct} }
 @article{arefeen_li_uddin_das_2023, title={MetaMorphosis: Task-oriented Privacy Cognizant Feature Generation for Multi-task Learning}, DOI={10.1145/3576842.3582372}, abstractNote={With the growth of computer vision applications, deep learning, and edge computing contribute to ensuring practical collaborative intelligence (CI) by distributing the workload among edge devices and the cloud. However, running separate single-task models on edge devices is inefficient regarding the required computational resource and time. In this context, multi-task learning allows leveraging a single deep learning model for performing multiple tasks, such as semantic segmentation and depth estimation on incoming video frames. This single processing pipeline generates common deep features that are shared among multi-task modules. However, in a collaborative intelligence scenario, generating common deep features has two major issues. First, the deep features may inadvertently contain input information exposed to the downstream modules (violating input privacy). Second, the generated universal features expose a piece of collective information than what is intended for a certain task, in which features for one task can be utilized to perform another task (violating task privacy). This paper proposes a novel deep learning-based privacy-cognizant feature generation process called “MetaMorphosis” that limits inference capability to specific tasks at hand. To achieve this, we propose a channel squeeze-excitation based feature metamorphosis module, Cross-SEC, to achieve distinct attention of all tasks and a de-correlation loss function with differential-privacy to train a deep learning model that produces distinct privacy-aware features as an output for the respective tasks. With extensive experimentation on four datasets consisting of diverse images related to scene understanding and facial attributes, we show that MetaMorphosis outperforms recent adversarial learning and universal feature generation methods by guaranteeing privacy requirements in an efficient way for image and video analytics.}, journal={PROCEEDINGS 8TH ACM/IEEE CONFERENCE ON INTERNET OF THINGS DESIGN AND IMPLEMENTATION, IOTDI 2023}, author={Arefeen, Md Adnan and Li, Zhouyu and Uddin, Md Yusuf Sarwar and Das, Anupam}, year={2023}, pages={288–300} }
 @article{zhang_li_das_2023, title={VoicePM: A Robust Privacy Measurement on Voice Anonymity}, DOI={10.1145/3558482.3590175}, abstractNote={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.}, journal={PROCEEDINGS OF THE 16TH ACM CONFERENCE ON SECURITY AND PRIVACY IN WIRELESS AND MOBILE NETWORKS, WISEC 2023}, author={Zhang, Shaohu and Li, Zhouyu and Das, Anupam}, year={2023}, pages={215–226} }
 @article{lee_logas_yang_li_barbosa_wang_das_2023, title={When and Why Do People Want Ad Targeting Explanations? Evidence from a Four-Week, Mixed-Methods Field Study}, ISSN={["1081-6011"]}, DOI={10.1109/SP46215.2023.00053}, journal={2023 IEEE SYMPOSIUM ON SECURITY AND PRIVACY, SP}, author={Lee, Hao-Ping and Logas, Jacob and Yang, Stephanie and Li, Zhouyu and Barbosa, Nata and Wang, Yang and Das, Sauvik}, year={2023}, pages={2903–2920} }
 @article{zhou_yu_li_gu_wang_2022, title={FedAegis: Edge-Based Byzantine-Robust Federated Learning for Heterogeneous Data}, ISSN={["2576-6813"]}, DOI={10.1109/GLOBECOM48099.2022.10000981}, abstractNote={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.}, journal={2022 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM 2022)}, author={Zhou, Fangtong and Yu, Ruozhou and Li, Zhouyu and Gu, Huayue and Wang, Xiaojian}, year={2022}, pages={3005–3010} }
 @article{wang_gu_li_zhou_yu_yang_2022, title={Why Riding the Lightning? Equilibrium Analysis for Payment Hub Pricing}, ISSN={["1550-3607"]}, DOI={10.1109/ICC45855.2022.9839171}, abstractNote={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.}, journal={IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2022)}, author={Wang, Xiaojian and Gu, Huayue and Li, Zhouyu and Zhou, Fangtong and Yu, Ruozhou and Yang, Dejun}, year={2022}, pages={5409–5414} }