Ismi Abidi (IIT Delhi), Ishan Nangia (MPI-SWS), Paarijaat Aditya (Nokia Bell Labs), Rijurekha Sen (IIT Delhi)

Companies providing services like cab sharing, e-commerce logistics and, food delivery are willing to instrument their vehicles for scaling up measurements of traffic congestion, travel time, road surface quality, air quality, etc.~cite{polmeasure}. Analyzing fine-grained sensors data from such large fleets can be highly beneficial; however, this sensor information reveals the locations and the number of vehicles in the deployed fleet. This sensitive data is of high business value to rival companies in the same business domain, e.g., Uber vs. Ola, Uber vs. Lyft in cab sharing, or Amazon vs. Alibaba in the e-commerce domain. This paper provides privacy guarantees for the scenario mentioned above using Gaussian Process Regression (GPR) based interpolation, Differential Privacy (DP), and Secure two-party computations (2PC). The sensed values from instrumented vehicle fleets are made available preserving fleet and client privacy, along with client utility. Our system has efficient latency and bandwidth overheads, even for resource-constrained mobile clients. To demonstrate our end-to-end system, we build a sample Android application that gives the least polluted route alternatives given a source-destination pair in a privacy preserved manner.

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All things Binary

Dr. Sergey Bratus, DARPA PI and Research Associate Professor at Dartmouth College

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Packet-Level Open-World App Fingerprinting on Wireless Traffic

Jianfeng Li (The Hong Kong Polytechnic University), Shuohan Wu (The Hong Kong Polytechnic University), Hao Zhou (The Hong Kong Polytechnic University), Xiapu Luo (The Hong Kong Polytechnic University), Ting Wang (Penn State), Yangyang Liu (The Hong Kong Polytechnic University), Xiaobo Ma (Xi'an Jiaotong University)

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Generating 3D Adversarial Point Clouds under the Principle of...

Bo Yang (Zhejiang University), Yushi Cheng (Tsinghua University), Zizhi Jin (Zhejiang University), Xiaoyu Ji (Zhejiang University) and Wenyuan Xu (Zhejiang University)

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DRIVETRUTH: Automated Autonomous Driving Dataset Generation for Security Applications

Raymond Muller (Purdue University), Yanmao Man (University of Arizona), Z. Berkay Celik (Purdue University), Ming Li (University of Arizona) and Ryan Gerdes (Virginia Tech)

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