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.

View More Papers

The Inconvenient Truths of Ground Truth for Binary Analysis

Jim Alves-Foss, Varsha Venugopal (University of Idaho)

Read More

Probe the Proto: Measuring Client-Side Prototype Pollution Vulnerabilities of...

Zifeng Kang (Johns Hopkins University), Song Li (Johns Hopkins University), Yinzhi Cao (Johns Hopkins University)

Read More

Explainable AI in Cybersecurity Operations: Lessons Learned from xAI...

Megan Nyre-Yu (Sandia National Laboratories), Elizabeth S. Morris (Sandia National Laboratories), Blake Moss (Sandia National Laboratories), Charles Smutz (Sandia National Laboratories), Michael R. Smith (Sandia National Laboratories)

Read More

ATTEQ-NN: Attention-based QoE-aware Evasive Backdoor Attacks

Xueluan Gong (Wuhan University), Yanjiao Chen (Zhejiang University), Jianshuo Dong (Wuhan University), Qian Wang (Wuhan University)

Read More