Fan DANG (党凡) is currently a research assistant professor (助理研究员) at Global Innovation Exchange (GIX). He was previously a postdoctoral research fellow at the School of Software, Tsinghua University, in collaboration with Prof. Yunhao Liu. He received his B.E. and Ph.D. degrees from Tsinghua University in 2013 and 2018.
He is a fan of open source and proudly a member of TUNA.
Ph.D. in Software Engineering, 2018
B.E. in Computer Software, 2013
Jan. 14, 2023 Fan DANG gave a talk on Equipment Area Network (EAN) at Industrial Internet Forum, CWSN 2022.
Dec. 2, 2022 Our two papers about TSN and the Industrial Internet were accepted by IEEE INFOCOM 2023.
Oct. 12, 2022 Our new paper was accepted by CML-IoT 2022.
Sep. 25, 2022 Our new paper was accepted by IEEE ICPADS 2022.
Sep. 21, 2022 Our paper entitled “DeliverSense: Efficient Delivery Drone Scheduling for Crowdsensing with Deep Reinforcement Learning” was selected as the best paper award of the CPD workshop at Ubicomp 2022.
Aug. 22, 2022 Our new paper and poster was accepted by ACM MobiCom 2022.
Apr. 5, 2022 Our new paper was accepted by IEEE ICDCS 2022.
Dec. 4, 2021 Our new paper was accepted by IEEE INFOCOM 2022.
Nov. 6, 2021 Fan DANG submitted an RFC draft about using SM2 in WebAuthn.
Oct. 13, 2021 Fan DANG gave a talk on TSN (time sensitive networking) at Yuzhou Big Data Lab.
Flexible manufacturing is one of the core goals of Industry 4.0 and brings new challenges to current industrial control systems. Our detailed field study on auto glass industry revealed that existing production lines are laborious to reconfigure, difficult to upscale, and costly to upgrade during production switching. Such inflexibility arises from the tight coupling of devices, controllers, and control tasks. In this work, we propose a new architecture for industrial control systems named Control-as-a-Service (CaaS). CaaS transfers and distributes control tasks from dedicated controllers into Time-Sensitive Networking (TSN) switches. By combining control and transmission functions in switches, CaaS virtualizes the industrial TSN network to one Programmable Logic Controller (PLC). We propose a set of techniques that realize end-to-end determinism for in-network industrial control and a joint task and traffic scheduling algorithm. We evaluate the performance of CaaS on testbeds based on real-world networked control systems. The results show that the idea of CaaS is feasible and effective, and CaaS achieves absolute packet delivery, 42-45% lower latency, and three orders of magnitude lower jitter. We believe CaaS is a meaningful step towards the distribution, virtualization, and servitization of industrial control.
Streaming services have billions of mobile subscribers, yet video piracy has cost service providers billions. Digital Rights Management (DRM), however, is still far from satisfactory. Unlike DRM, which attempts to prohibit the creation of pirated copies, fingerprinting may be used to track out the source of piracy. Nevertheless, the idea of piracy tracing is not widely used at the moment, since existing fingerprinting-based streaming systems fail to serve numerous users. In this paper, we present the design and evaluation of StreamingTag, a scalable piracy tracing system for mobile streaming services. StreamingTag adopts a segment-level fingerprint embedding scheme to remove the need of re-embedding the fingerprint into the video for each new viewer. The key innovations of StreamingTag include a scalable and CDN-friendly delivery framework, a polarized and randomized SVD watermarking scheme suitable for short segments, and a collusion-resistant fingerprinting scheme optimized for large-scale streaming services. Experiment results show the good QoS of StreamingTag in terms of preparation latency, bandwidth consumption, and video fidelity. Compared with existing SVD watermarking schemes, the proposed watermarking scheme improves the watermark extraction accuracy by 2.25x at most and 1.5x on average. Compared with existing collusion-resistant fingerprinting schemes, the proposed scheme catches more colluders and improves the recall rate by 26%.
The widespread of smart devices and the development of mobile networks bring the growing popularity of live streaming services worldwide. In addition to the video and audio transmission, a lot more media content is sent to the audiences as well, including player statistics for a sports stream, subtitles for living news, etc. However, due to the diverse transmission process between live streams and other media content, the synchronization of them has grown to be a great challenge. Unfortunately, the existing commercial solutions are not universal, which require specific server cloud services or CDN and limit the users’ free choices of web infrastructures. To address the issue, we propose a lightweight universal event-synchronizing solution for live streaming, called LSync, which inserts a series of audio signals containing metadata into the original audio stream. It brings no modification to the original live broadcast process and thus fits prevalent live broadcast infrastructure. Evaluations on the real system show that the proposed solution reduces the signal processing delay by at most 5.62% of an audio buffer length in mobile phones and ensures real-time signal processing. It also achieves a data rate of 156.25 bps in a specific configuration and greatly outperforms recent works.
As the most widely applied public-key cryptographic algorithm, RSA is now integrated into many low-cost devices such as IoT devices. Due to the limited resource, most low-cost devices only ship a 2048-bit multiplier, making the longest supported private key length as 2048 bits. Unfortunately, 2048-bit RSA keys are gradually considered insecure. Utilizing the existing 2048-bit multiplier is challenging because a 4096-bit message cannot be stored in the multiplier. In this paper, we perform a thorough study of RSA and propose a new method that achieves the 4096-bit RSA cryptography with the existing hardware. We use the Montgomery modular multiplication and the Chinese Remainder Theorem to reduce the computational cost and construct the necessary components to compute the RSA private key operation. To further validate the correctness of the method and evaluate its performance, we implement this method on a micro-controller and build a testbed named CanoKey with three commonly used cryptography protocols. The result shows that our method is over 200x faster than the naïve method, a.k.a., software-based big number multiplications.
In this paper, we present our endeavor in understanding fileless attacks on Linux-based IoT devices in the wild. Over a span of twelve months, we deploy 4 hardware IoT honeypots and 108 specially designed software IoT honeypots, and successfully attract a wide variety of real-world IoT attacks. We present our measurement study on these attacks, with a focus on fileless attacks, including the prevalence, exploits, environments, and impacts.
Automated Fare Collection (AFC) systems have been globally deployed for decades, particularly in the public transportation network where the transit fee is calculated based on the length of the trip. In this paper, we identify a novel paradigm of attacks, called LessPay, against modern distance-based pricing AFC systems, enabling users to pay much less than what they are supposed to be charged.
Publication Co-Chair of ACM TURC 2021, 2020
Mentor of Open Source Promotion Plan 2022, 2021, 2020
Session Chair of IEEE ICPADS 2022
Session Chair of IEEE INFOCOM 2020
TPC Member of IEEE INFOCOM 2020
Reviewer for IEEE THMS, JCST, Cluster Computing, EAI MobiQuitous, Sensors Journal
2022 Best paper award of the CPD workshop at Ubicomp 2022
2019 ACM SIGCOMM China Doctoral Dissertation Award
2014 Museum of Science and Technology Development Award, China Science and Technology Museum
2013-2014 Vice President, Student Association of Science and Technology, Tsinghua University
2013 Champion, Solve For Tomorrow 2013, China
2010-2012 First Class Scholarship for Overall Excellence, Tsinghua University