IEEE INFOCOM 2023
Mixup Training for Generative Models to Defend Membership Inference Attacks
Zhe Ji, Qiansiqi Hu and Liyao Xiang (Shanghai Jiao Tong University, China); Chenghu Zhou (Chinese Academy of Sciences, China)
Speaker Zhe Ji (Shanghai Jiao Tong University)
Zhe Ji is a master student at Shanghai Jiao Tong University. He graduated from Shanghai Jiao Tong University with a bachelor's degree in computer science and technology. His current research interests mainly focus on privacy issues in machine learning.
Spotting Deep Neural Network Vulnerabilities in Mobile Traffic Forecasting with an Explainable AI Lens
Serly Moghadas (IMDEA Networks, Spain); Claudio Fiandrino and Alan Collet (IMDEA Networks Institute, Spain); Giulia Attanasio (IMDEA Networks, Spain); Marco Fiore and Joerg Widmer (IMDEA Networks Institute, Spain)
to adversarial attacks which undermine their applicability in production networks. In this paper, we conduct a first in-depth study of the vulnerabilities of DNNs for large-scale mobile traffic forecasting. We propose DeExp, a new tool that leverages EXplainable Artificial Intelligence (XAI) to understand which Base Stations (BSs) are more influential for forecasting from a spatio-temporal perspective. This is challenging as existing XAI techniques are usually applied to computer vision or natural language processing and need to be adapted to the mobile network context. Upon identifying the more influential BSs, we run state-of-the art Adversarial Machine Learning (AML) techniques on those BSs and measure the accuracy degradation of the predictors. Extensive evaluations with real-world mobile traffic traces pinpoint that attacking BSs relevant to the predictor significantly degrades its accuracy across all the scenarios.
Speaker Claudio Fiandrino (IMDEA Networks Institute)
Claudio Fiandrino is a senior researcher at IMDEA Networks Institute. He obtained his Ph.D. degree at the University of Luxembourg in 2016. Claudio has received numerous awards for his research, including a Fulbright scholarship in 2022, a 5-year long Spanish Juan de la Cierva grants and several Best Paper Awards. He is member of IEEE and ACM, serves in the Technical Program Committee (TPC) of several international IEEE and ACM conferences and regularly participates in the organization of events. Claudio is member of the Editorial Board of IEEE Networking Letters and Chair of the IEEE ComSoc EMEA Awards Committee. His primary research interests include explainable and robust AI for mobile networks, next generation mobile networks, and multi-access edge computing.
FeatureSpy: Detecting Learning-Content Attacks via Feature Inspection in Secure Deduplicated Storage
Jingwei Li (University of Electronic Science and Technology of China, China); Yanjing Ren and Patrick Pak-Ching Lee (The Chinese University of Hong Kong, Hong Kong); Yuyu Wang (University of Electronic Science and Technology of China, China); Ting Chen (University of Electronic Science and Technology of China (UESTC), China); Xiaosong Zhang (University of Electronic Science and Technology of China, China)
Speaker Patrick P. C. Lee (The Chinese University of Hong Kong)
Patrick Lee is now a Professor of the Department of Computer Science and Engineering at the Chinese University of Hong Kong. His research interests are in storage systems, distributed systems and networks, and cloud computing.
Fast Generation-Based Gradient Leakage Attacks against Highly Compressed Gradients
Dongyun Xue, Haomiao Yang, Mengyu Ge and Jingwei Li (University of Electronic Science and Technology of China, China); Guowen Xu (Nanyang Technological University, Singapore); Hongwei Li (University of Electronic Science and Technology of China, China)
Speaker Dongyun Xue
Dongyun Xue is a graduate student at the University of Electronic Science and Technology of China, with a major research focus on artificial intelligence security.
De-anonymization Attacks on Metaverse
Yan Meng, Yuxia Zhan, Jiachun Li, Suguo Du and Haojin Zhu (Shanghai Jiao Tong University, China); Sherman Shen (University of Waterloo, Canada)
Speaker Yan Meng (Shanghai Jiao Tong University)
DisProTrack: Distributed Provenance Tracking over Serverless Applications
Utkalika Satapathy and Rishabh Thakur (Indian Institute of Technology Kharagpur, India); Subhrendu Chattopadhyay (Institute for Developemnt and Research in Banking Technologies, India); Sandip Chakraborty (Indian Institute of Technology Kharagpur, India)
Speaker Utkalika Satapathy(Indian Institute of Technology, Kharagpur, India)
I am a Research Scholar in the Department of Computer Science and Engineering at the Indian Institute of Information Technology (IIT) Kharagpur, India. Under the supervision of Prof. Sandip Chakraborty, I am pursuing my Ph.D.
In addition, I am a member of the research group Ubiquitous Networked Systems Lab (UbiNet) at IIT Kharagpur, India. As for my research interests, they revolve around the areas of Systems, Provenance Tracking, and Distributed systems.
ASTrack: Automatic detection and removal of web tracking code with minimal functionality loss
Ismael Castell-Uroz (Universitat Politècnica de Catalunya, Spain); Kensuke Fukuda (National Institute of Informatics, Japan); Pere Barlet-Ros (Universitat Politècnica de Catalunya, Spain)
Speaker Ismael Castell-Uroz (Universitat Politècnica de Catalunya)
Ismael Castell-Uroz is a Ph.D. student at the Computer Architecture Department of the Universitat Politècnica de Catalunya (UPC), Barcelona, Spain, where he received the B.Sc. degree in Computer Science in 2008 and the M.Sc. degree in Computer Architecture, Networks, and Systems in 2010. He has several years of experience in network and system administration and currently holds a Projects Scholarship at UPC. His expertise and research interest are in computer networks, especially in the field of network monitoring, anomaly detection, internet privacy and web tracking.
Secure Middlebox Channel over TLS and its Resiliency against Middlebox Compromise
Kentaro Kita, Junji Takemasa, Yuki Koizumi and Toru Hasegawa (Osaka University, Japan)
Speaker Kentaro Kita (Osaka University)
Kentaro Kita received his Ph.D. in information science from Osaka University. His research interests include privacy, anonymity, security, and future networking architecture.
Enable Batteryless Flex-sensors via RFID Tags
Mengning Li (North Carolina State University, USA)
Speaker Mengning Li
Mengning Li is a first-year Ph.D. student at North Carolina State University, where she is fortunate to be advised by Prof. Wenye Wang. Her research interest mainly lies in wireless sensing.
TomoID: A Scalable Approach to Device Free Indoor Localization via RFID Tomography
Yang-Hsi Su and Jingliang Ren (University of Michigan, USA); Zi Qian (Tsinghua University, China); David Fouhey and Alanson Sample (University of Michigan, USA)
Speaker Yang-Hsi Su (University of Michigan - Ann Arbor)
A 3rd year PhD student in the Interactive Sensing and Computing Lab lead by Prof. Alanson Sample at the University of Michigan. Mainly focuses on RF sensing and RF localization.
Extracting Spatial Information of IoT Device Events for Smart Home Safety Monitoring
Yinxin Wan, Xuanli Lin, Kuai Xu, Feng Wang and Guoliang Xue (Arizona State University, USA)
Speaker Yinxin Wan (Arizona State University)
Yinxin Wan is a final-year Ph.D. candidate majoring in Computer Science at Arizona State University. He obtained his B.E. degree from the University of Science and Technology of China in 2018. His research interests include cybersecurity, IoT, network measurement, and data-driven networked systems.
RT-BLE: Real-time Multi-Connection Scheduling for Bluetooth Low Energy
Yeming Li and Jiamei Lv (Zhejiang University, China); Borui Li (Southeast University, China); Wei Dong (Zhejiang University, China)
Speaker Yeming Li (Zhejiang University)
Yeming Li received the B.S. degree in computer science from Zhejiang University of Technoligy, in 2020.
He is currently pursuing the Ph.D. degree with Zhejiang University.
His research interests include Internet of Things and wireless protocols.
DIAMOND: Taming Sample and Communication Complexities in Decentralized Bilevel Optimization
Peiwen Qiu, Yining Li and Zhuqing Liu (The Ohio State University, USA); Prashant Khanduri (University of Minnesota, USA); Jia Liu and Ness B. Shroff (The Ohio State University, USA); Elizabeth Serena Bentley (AFRL, USA); Kurt Turck (United States Air Force Research Labs, USA)
Speaker Peiwen Qiu (The Ohio State University)
Peiwen Qiu is a Ph.D. student at The Ohio State University under the supervision of Prof. Jia (Kevin) Liu. Her research interests include but are not limited to optimization theory and algorithms for bilevel optimization, decentralized bilevel optimization and federated learning.
PipeMoE: Accelerating Mixture-of-Experts through Adaptive Pipelining
Shaohuai Shi (Harbin Institute of Technology, Shenzhen, China); Xinglin Pan and Xiaowen Chu (Hong Kong Baptist University, Hong Kong); Bo Li (Hong Kong University of Science and Technology, Hong Kong)
Speaker Shaohuai Shi
Shaohuai Shi is currently an Assistant Professor at the School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen. Previously, he was a Research Assistant Professor at the Department of Computer Science & Engineering of The Hong Kong University of Science and Technology. His current research focus is distributed machine learning systems.
Accelerating Distributed K-FAC with Efficient Collective Communication and Scheduling
Lin Zhang (Hong Kong University of Science and Technology, Hong Kong); Shaohuai Shi (Harbin Institute of Technology, Shenzhen, China); Bo Li (Hong Kong University of Science and Technology, Hong Kong)
Speaker Lin Zhang (Hong Kong University of Science and Technology)
Lin Zhang is currently pursuing the Ph.D. degree in the Department of Computer Science and Engineering at the Hong Kong University of Science and Technology. His research interests include machine learning systems and algorithms, with a special focus on distributed DNNs training, and second-order optimization methods.
DAGC: Data-aware Adaptive Gradient Compression
Rongwei Lu (Tsinghua University, China); Jiajun Song (Dalian University of Technology, China); Bin Chen (Harbin Institute of Technology, Shenzhen, China); Laizhong Cui (Shenzhen University, China); Zhi Wang (Tsinghua University, China)
In this study, we first derive a function from capturing the correlation between the number of training iterations for a model to converge to the same accuracy, and the compression ratios at different workers; This function particularly shows that workers with larger data volumes should be assigned with higher compression ratios to guarantee better accuracy. Then, we formulate the assignment of compression ratios to the workers as an n-variables chi-square nonlinear optimization problem under fixed and limited total communication constrain. We propose an adaptive gradients compression strategy called DAGC, which assigns each worker a different compression ratio according to their data volumes. Our experiments confirm that DAGC can achieve better performance facing highly imbalanced data volume distribution and restricted communication.
Speaker Rongwei Lu (Tsinghua University)
Rongwei is a second-year Master's student in Computer Technology at Tsinghua University, advised by Prof. Zhi Wang. His research interests are to accelerating machine learning training from communication and computation. He was a research intern in System Research Group of MSRA. This paper is his first published paper.