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Tor exit blocking, in which websites disallow clients arriving from Tor, is a growing and potentially existential threat to the anonymity network. This paper introduces HebTor, a new and robust architecture for exit bridges—short-lived proxies that serve as alternative egress points for Tor. A key insight of HebTor is that exit bridges can operate as Tor onion services, allowing any device that can create outbound TCP connections to serve as an exit bridge, regardless of the presence of NATs and/or firewalls. HebTor employs a micro-payment system that compensates exit bridge operators for their services, and a privacy-preserving reputation scheme that prevents freeloading. We show that HebTor effectively thwarts server-side blocking of Tor, and we describe the security, privacy, and legal implications of our design. 

Despite the emergence of probabilistic logic programming (PLP) languages for data driven applications, there are currently no debugging tools based on provenance for PLP programs. In this paper, we propose a novel provenance model and system, called P3 (Provenance for Probabilistic logic Programs) for analyzing PLP programs. P3 enables four types of provenance queries: traditional explanation queries, queries for finding the set of most important derivations within an approximate error, top-K most influential queries, and modification queries that enable us to modify tuple probabilities with fewest modifications to program or input data. We apply these queries into real-world scenarios and present theoretical analysis and practical algorithms for such queries. We have developed a prototype of P3, and our evaluation on real-world data demonstrates that the system can support a wide-range of provenance queries with explainable results. Moreover, the system maintains provenance and execute queries efficiently with low overhead.