Dovetail: Stronger Anonymity in Next-Generation Internet Routing

Current low-latency anonymity systems use complex overlay networks to conceal a user's IP address, introducing significant latency and network efficiency penalties compared to normal Internet usage. Rather than obfuscating network identity through higher level protocols, we propose a more direct solution: a routing protocol that allows communication without exposing network identity, providing a strong foundation for Internet privacy, while allowing identity to be defined in those higher level protocols where it adds value. Given current research initiatives advocating "clean slate" Internet designs, an opportunity exists to design an internetwork layer routing protocol that decouples identity from network location and thereby simplifies the anonymity problem. Recently, Hsiao et al. proposed such a protocol (LAP), but it does not protect the user against a local eavesdropper or an untrusted ISP, which will not be acceptable for many users. Thus, we propose Dovetail, a next-generation Internet routing protocol that provides anonymity against an active attacker located at any single point within the network, including the user's ISP. A major design challenge is to provide this protection without including an application-layer proxy in data transmission. We address this challenge in path construction by using a matchmaker node (an end host) to overlap two path segments at a dovetail node (a router). The dovetail then trims away part of the path so that data transmission bypasses the matchmaker. Additional design features include the choice of many different paths through the network and the joining of path segments without requiring a trusted third party. We develop a systematic mechanism to measure the topological anonymity of our designs, and we demonstrate the privacy and efficiency of our proposal by simulation, using a model of the complete Internet at the AS-level

Prosecuting Dark Net Drug Marketplace Operators Under the Federal Crack House Statute

Over 70,000 Americans died as the result of a drug overdose in 2017, a record year following a record year. Amidst this crisis, the popularity of drug marketplaces on what has been called the “dark net” has exploded. Illicit substances are sold freely on such marketplaces, and the anonymity these marketplaces provide has proved troublesome for law enforcement. Law enforcement has responded by taking down several of these marketplaces and prosecuting their creators, such as Ross Ulbricht of the former Silk Road. Prosecutors have typically leveled conspiracy charges against the operators of these marketplaces—in Ulbricht’s case, alleging a single drug conspiracy comprising Ulbricht and the thousands of vendors on the Silk Road. This Note argues that the conspiracy to distribute narcotics charge is a poor conceptual fit for the behavior of operators of typical dark net drug marketplaces, and that the federal “crack house” statute provides a better charge. Though charging these operators under the crack house statute would be a novel approach, justice is best served when the crime accurately describes the behavior, as the crack house statute does in proscribing what dark net drug marketplace operators like Ulbricht do

Bitcoin over Tor isn't a good idea

Bitcoin is a decentralized P2P digital currency in which coins are generated by a distributed set of miners and transaction are broadcasted via a peer-to-peer network. While Bitcoin provides some level of anonymity (or rather pseudonymity) by encouraging the users to have any number of random-looking Bitcoin addresses, recent research shows that this level of anonymity is rather low. This encourages users to connect to the Bitcoin network through anonymizers like Tor and motivates development of default Tor functionality for popular mobile SPV clients. In this paper we show that combining Tor and Bitcoin creates an attack vector for the deterministic and stealthy man-in-the-middle attacks. A low-resource attacker can gain full control of information flows between all users who chose to use Bitcoin over Tor. In particular the attacker can link together user's transactions regardless of pseudonyms used, control which Bitcoin blocks and transactions are relayed to the user and can \ delay or discard user's transactions and blocks. In collusion with a powerful miner double-spending attacks become possible and a totally virtual Bitcoin reality can be created for such set of users. Moreover, we show how an attacker can fingerprint users and then recognize them and learn their IP address when they decide to connect to the Bitcoin network directly.Comment: 11 pages, 4 figures, 4 table

Pretty Private Group Management

Group management is a fundamental building block of today's Internet applications. Mailing lists, chat systems, collaborative document edition but also online social networks such as Facebook and Twitter use group management systems. In many cases, group security is required in the sense that access to data is restricted to group members only. Some applications also require privacy by keeping group members anonymous and unlinkable. Group management systems routinely rely on a central authority that manages and controls the infrastructure and data of the system. Personal user data related to groups then becomes de facto accessible to the central authority. In this paper, we propose a completely distributed approach for group management based on distributed hash tables. As there is no enrollment to a central authority, the created groups can be leveraged by various applications. Following this paradigm we describe a protocol for such a system. We consider security and privacy issues inherently introduced by removing the central authority and provide a formal validation of security properties of the system using AVISPA. We demonstrate the feasibility of this protocol by implementing a prototype running on top of Vuze's DHT

Exploiting Anonymity in Approximate Linear Programming: Scaling to Large Multiagent MDPs (Extended Version)

Many exact and approximate solution methods for Markov Decision Processes (MDPs) attempt to exploit structure in the problem and are based on factorization of the value function. Especially multiagent settings, however, are known to suffer from an exponential increase in value component sizes as interactions become denser, meaning that approximation architectures are restricted in the problem sizes and types they can handle. We present an approach to mitigate this limitation for certain types of multiagent systems, exploiting a property that can be thought of as "anonymous influence" in the factored MDP. Anonymous influence summarizes joint variable effects efficiently whenever the explicit representation of variable identity in the problem can be avoided. We show how representational benefits from anonymity translate into computational efficiencies, both for general variable elimination in a factor graph but in particular also for the approximate linear programming solution to factored MDPs. The latter allows to scale linear programming to factored MDPs that were previously unsolvable. Our results are shown for the control of a stochastic disease process over a densely connected graph with 50 nodes and 25 agents.Comment: Extended version of AAAI 2016 pape

SECMACE: Scalable and Robust Identity and Credential Management Infrastructure in Vehicular Communication Systems

Several years of academic and industrial research efforts have converged to a common understanding on fundamental security building blocks for the upcoming Vehicular Communication (VC) systems. There is a growing consensus towards deploying a special-purpose identity and credential management infrastructure, i.e., a Vehicular Public-Key Infrastructure (VPKI), enabling pseudonymous authentication, with standardization efforts towards that direction. In spite of the progress made by standardization bodies (IEEE 1609.2 and ETSI) and harmonization efforts (Car2Car Communication Consortium (C2C-CC)), significant questions remain unanswered towards deploying a VPKI. Deep understanding of the VPKI, a central building block of secure and privacy-preserving VC systems, is still lacking. This paper contributes to the closing of this gap. We present SECMACE, a VPKI system, which is compatible with the IEEE 1609.2 and ETSI standards specifications. We provide a detailed description of our state-of-the-art VPKI that improves upon existing proposals in terms of security and privacy protection, and efficiency. SECMACE facilitates multi-domain operations in the VC systems and enhances user privacy, notably preventing linking pseudonyms based on timing information and offering increased protection even against honest-but-curious VPKI entities. We propose multiple policies for the vehicle-VPKI interactions, based on which and two large-scale mobility trace datasets, we evaluate the full-blown implementation of SECMACE. With very little attention on the VPKI performance thus far, our results reveal that modest computing resources can support a large area of vehicles with very low delays and the most promising policy in terms of privacy protection can be supported with moderate overhead.Comment: 14 pages, 9 figures, 10 tables, IEEE Transactions on Intelligent Transportation System