Eric Pauley and Patrick McDaniel (University of Wisconsin–Madison)

Measurement of network data received from or transmitted over the public Internet has yielded a myriad of insights towards improving the security and privacy of deployed services. Yet, the collection and analysis of this data necessarily involves the processing of data that could impact human subjects, and anonymization often destroys the very phenomena under study. As a result, Internet measurement faces the unique challenge of studying data from human subjects who could not conceivably consent to its collection, and yet the measurement community has tacitly concluded that such measurement is beneficial and even necessary for its positive impacts. We are thus at an impasse: academics and practitioners routinely collect and analyze sensitive user data, and yet there exists no cohesive set of ethical norms for the community that justifies these studies. In this work, we examine the ethical considerations of Internet traffic measurement and analysis, analyzing the ethical considerations and remediations in prior works and general trends in the community. We further analyze ethical expectations in calls-for-papers, finding a general lack of cohesion across venues. Through our analysis and recommendations, we hope to inform future studies and venue expectations towards maintaining positive impact while respecting and protecting end users.

View More Papers

He-HTLC: Revisiting Incentives in HTLC

Sarisht Wadhwa (Duke University), Jannis Stoeter (Duke University), Fan Zhang (Duke University, Yale University), Kartik Nayak (Duke University)

Read More

On the Anonymity of Peer-To-Peer Network Anonymity Schemes Used...

Piyush Kumar Sharma (imec-COSIC, KU Leuven), Devashish Gosain (Max Planck Institute for Informatics), Claudia Diaz (Nym Technologies, SA and imec-COSIC, KU Leuven)

Read More

MetaWave: Attacking mmWave Sensing with Meta-material-enhanced Tags

Xingyu Chen (University of Colorado Denver), Zhengxiong Li (University of Colorado Denver), Baicheng Chen (University of California San Diego), Yi Zhu (SUNY at Buffalo), Chris Xiaoxuan Lu (University of Edinburgh), Zhengyu Peng (Aptiv), Feng Lin (Zhejiang University), Wenyao Xu (SUNY Buffalo), Kui Ren (Zhejiang University), Chunming Qiao (SUNY at Buffalo)

Read More

BARS: Local Robustness Certification for Deep Learning based Traffic...

Kai Wang (Tsinghua University), Zhiliang Wang (Tsinghua University), Dongqi Han (Tsinghua University), Wenqi Chen (Tsinghua University), Jiahai Yang (Tsinghua University), Xingang Shi (Tsinghua University), Xia Yin (Tsinghua University)

Read More