Tianhao Wang (Purdue University), Milan Lopuhaä-Zwakenberg (Eindhoven University of Technology), Zitao Li (Purdue University), Boris Skoric (Eindhoven University of Technology), Ninghui Li (Purdue University)

Local Differential Privacy (LDP) protects user privacy from the data collector. LDP protocols have been increasingly deployed in the industry. A basic building block is frequency oracle (FO) protocols, which estimate frequencies of values. While several FO protocols have been proposed, the design goal does not lead to optimal results for answering many queries. In this paper, we show that adding post-processing steps to FO protocols by exploiting the knowledge that all individual frequencies should be non-negative and they sum up to one can lead to significantly better accuracy for a wide range of tasks, including frequencies of individual values, frequencies of the most frequent values, and frequencies of subsets of values. We consider 10 different methods that exploit this knowledge differently. We establish theoretical relationships between some of them and conducted extensive experimental evaluations to understand which methods should be used for different query tasks.

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Practical Traffic Analysis Attacks on Secure Messaging Applications

Alireza Bahramali (University of Massachusetts Amherst), Amir Houmansadr (University of Massachusetts Amherst), Ramin Soltani (University of Massachusetts Amherst), Dennis Goeckel (University of Massachusetts Amherst), Don Towsley (University of Massachusetts Amherst)

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Into the Deep Web: Understanding E-commerce Fraud from Autonomous...

Peng Wang (Indiana University Bloomington), Xiaojing Liao (Indiana University Bloomington), Yue Qin (Indiana University Bloomington), XiaoFeng Wang (Indiana University Bloomington)

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Are You Going to Answer That? Measuring User Responses...

Imani N. Sherman (University of Florida), Jasmine D. Bowers (University of Florida), Keith McNamara Jr. (University of Florida), Juan E. Gilbert (University of Florida), Jaime Ruiz (University of Florida), Patrick Traynor (University of Florida)

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FlowPrint: Semi-Supervised Mobile-App Fingerprinting on Encrypted Network Traffic

Thijs van Ede (University of Twente), Riccardo Bortolameotti (Bitdefender), Andrea Continella (UC Santa Barbara), Jingjing Ren (Northeastern University), Daniel J. Dubois (Northeastern University), Martina Lindorfer (TU Wien), David Choffnes (Northeastern University), Maarten van Steen (University of Twente), Andreas Peter (University of Twente)

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