Liang Wang, Hyojoon Kim, Prateek Mittal, Jennifer Rexford (Princeton University)

In conventional DNS, or Do53, requests and responses are sent in cleartext. Thus, DNS recursive resolvers or any on-path adversaries can access privacy-sensitive information. To address this issue, several encryption-based approaches (e.g., DNS-over-HTTPS) and proxy-based approaches (e.g., Oblivious DNS) were proposed. However, encryption-based approaches put too much trust in recursive resolvers. Proxy-based approaches can help hide the client’s identity, but sets a higher deployment barrier while also introducing noticeable performance overhead. We propose PINOT, a packet-header obfuscation system that runs entirely in the data plane of a programmable network switch, which provides a lightweight, low-deployment-barrier anonymization service for clients sending and receiving DNS packets. PINOT does not require any modification to the DNS protocol or additional client software installation or proxy setup. Yet, it can also be combined with existing approaches to provide stronger privacy guarantees. We implement a PINOT prototype on a commodity switch, deploy it in a campus network, and present results on protecting user identity against public DNS services.

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Sn4ke: Practical Mutation Analysis of Tests at Binary Level

Mohsen Ahmadi (Arizona State University), Pantea Kiaei (Worcester Polytechnic Institute), Navid Emamdoost (University of Minnesota)

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Data Poisoning Attacks to Deep Learning Based Recommender Systems

Hai Huang (Tsinghua University), Jiaming Mu (Tsinghua University), Neil Zhenqiang Gong (Duke University), Qi Li (Tsinghua University), Bin Liu (West Virginia University), Mingwei Xu (Tsinghua University)

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SquirRL: Automating Attack Analysis on Blockchain Incentive Mechanisms with...

Charlie Hou (CMU, IC3), Mingxun Zhou (Peking University), Yan Ji (Cornell Tech, IC3), Phil Daian (Cornell Tech, IC3), Florian Tramèr (Stanford University), Giulia Fanti (CMU, IC3), Ari Juels (Cornell Tech, IC3)

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Your Phone is My Proxy: Detecting and Understanding Mobile...

Xianghang Mi (University at Buffalo), Siyuan Tang (Indiana University Bloomington), Zhengyi Li (Indiana University Bloomington), Xiaojing Liao (Indiana University Bloomington), Feng Qian (University of Minnesota Twin Cities), XiaoFeng Wang (Indiana University Bloomington)

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