The fundamental challenge of configuration verification in an interdomain network is privacy because each autonomous system (AS) treats its network configuration files as private information and is not willing to share them with others. In this paper, we present IVeri, a scalable privacypreserving interdomain configuration verification system based on secure multi-party computation. IVeri supports privacypreserving verification and scalable verification via the following designs: (1) a verification algorithm that meets secure multi-party computation security requirements, (2) a data aggregator that reduces communication overhead while preserving privacy, (3) an algorithm that accelerates the simulation process based on routing algebra, and (4) an incremental verification algorithm that handles minor configuration changes. Extensive experiments with open-source datasets demonstrate that IVeri's optimization techniques significantly improve scalability, enabling the verification of large networks with 1000 ASes in under 3 hours, which outperforms state-of-the-art solutions.

Interdomain network configuration errors can lead to disastrous financial and social consequences. Although substantial progress has been made in using formal methods to verify whether network configurations conform to certain properties, current tools focus on a single network. The fundamental challenge of configuration verification in an interdomain network is privacy, because each autonomous system (AS) treats its network configuration files as private information and is not willing to share it with others. In this paper, we take a first step toward interdomain network configuration verification and propose InCV, a privacy-preserving interdomain configuration verification system based on data-oblivious computation. Given an interdomain network, InCV allows ASes to collaboratively simulate the running of the network and verify the resulting interdomain routing information base (RIB) without revealing their network configurations to any party. Preliminary evaluation using real-world topologies and synthetic network configurations shows that InCV can verify an interdomain network of 32 ASes within ∼ 52 minutes with reasonable overhead.

Vehicle localization service is a fundamental component of intelligent transportation systems. The widely used satellite navigation systems perform poorly in urban areas because the lines of sight to satellites are blocked by complex terrain characteristics, e.g., buildings, elevated streets and interchanges. In this paper, we design RadioLoc, a novel system achieving accurate, efficient, all-terrain vehicle localization with two key design points. First, RadioLoc harvests the frequency modulation (FM) signal, which has higher availability than satellite signal in complex terrains, as the signal source for localization. Second, RadioLoc integrates modern machine learning techniques into the processing of FM signals to efficiently learn the accurate vehicle localization in all-terrain environments. We validate the feasibility of FM-based vehicle localization and corresponding challenges and practical issues via field tests (e.g., signal distortion, signal inconsistency and limited in- vehicle radio bandwidth), and develop a series of advanced techniques in RadioLoc to address them, including adaptive batching, frequency sweeping, a novel multipath delay spread filter, a reconstructive PCA denoiser and a tailored FM feature extractor. We then develop a generic, modular localization module in RadioLoc, and design different learning-based 3D position identification algorithms for this module. We implement a prototype of RadioLoc and perform extensive field experiments to evaluate its efficiency and efficacy. Results show that (1) RadioLoc achieves a real-time localization latency of less than 100 milliseconds; (2) RadioLoc achieves a worst-case localization accuracy of 99.6% even in an underground parking lot, and (3) the horizontal error of RadioLoc is only one sixth of a dedicated GPS device even when the vehicle is moving at a high-speed (i.e., 80 km/h) in a complex highway scenario.