Systematizing Decentralization and Privacy: Lessons from 15 Years of Research and Deployments

Decentralized systems are a subset of distributed systems where multiple authorities control different components and no authority is fully trusted by all. This implies that any component in a decentralized system is potentially adversarial. We revise fifteen years of research on decentralization and privacy, and provide an overview of key systems, as well as key insights for designers of future systems. We show that decentralized designs can enhance privacy, integrity, and availability but also require careful trade-offs in terms of system complexity, properties provided, and degree of decentralization. These trade-offs need to be understood and navigated by designers. We argue that a combination of insights from cryptography, distributed systems, and mechanism design, aligned with the development of adequate incentives, are necessary to build scalable and successful privacy-preserving decentralized systems

POISED: Spotting Twitter Spam Off the Beaten Paths

Cybercriminals have found in online social networks a propitious medium to spread spam and malicious content. Existing techniques for detecting spam include predicting the trustworthiness of accounts and analyzing the content of these messages. However, advanced attackers can still successfully evade these defenses. Online social networks bring people who have personal connections or share common interests to form communities. In this paper, we first show that users within a networked community share some topics of interest. Moreover, content shared on these social network tend to propagate according to the interests of people. Dissemination paths may emerge where some communities post similar messages, based on the interests of those communities. Spam and other malicious content, on the other hand, follow different spreading patterns. In this paper, we follow this insight and present POISED, a system that leverages the differences in propagation between benign and malicious messages on social networks to identify spam and other unwanted content. We test our system on a dataset of 1.3M tweets collected from 64K users, and we show that our approach is effective in detecting malicious messages, reaching 91% precision and 93% recall. We also show that POISED's detection is more comprehensive than previous systems, by comparing it to three state-of-the-art spam detection systems that have been proposed by the research community in the past. POISED significantly outperforms each of these systems. Moreover, through simulations, we show how POISED is effective in the early detection of spam messages and how it is resilient against two well-known adversarial machine learning attacks

Supporting Large Scale Communication Systems on Infrastructureless Networks Composed of Commodity Mobile Devices: Practicality, Scalability, and Security.

Infrastructureless Delay Tolerant Networks (DTNs) composed of commodity mobile devices have the potential to support communication applications resistant to blocking and censorship, as well as certain types of surveillance. In this thesis we study the utility, practicality, robustness, and security of these networks. We collected two sets of wireless connectivity traces of commodity mobile devices with different granularity and scales. The first dataset is collected through active installation of measurement software on volunteer users' own smartphones, involving 111 users of a DTN microblogging application that we developed. The second dataset is collected through passive observation of WiFi association events on a university campus, involving 119,055 mobile devices. Simulation results show consistent message delivery performances of the two datasets. Using an epidemic flooding protocol, the large network achieves an average delivery rate of 0.71 in 24 hours and a median delivery delay of 10.9 hours. We show that this performance is appropriate for sharing information that is not time sensitive, e.g., blogs and photos. We also show that using an energy efficient variant of the epidemic flooding protocol, even the large network can support text messages while only consuming 13.7% of a typical smartphone battery in 14 hours. We found that the network delivery rate and delay are robust to denial-of-service and censorship attacks. Attacks that randomly remove 90% of the network participants only reduce delivery rates by less than 10%. Even when subjected to targeted attacks, the network suffered a less than 10% decrease in delivery rate when 40% of its participants were removed. Although structurally robust, the openness of the proposed network introduces numerous security concerns. The Sybil attack, in which a malicious node poses as many identities in order to gain disproportionate influence, is especially dangerous as it breaks the assumption underlying majority voting. Many defenses based on spatial variability of wireless channels exist, and we extend them to be practical for ad hoc networks of commodity 802.11 devices without mutual trust. We present the Mason test, which uses two efficient methods for separating valid channel measurement results of behaving nodes from those falsified by malicious participants.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120779/1/liuyue_1.pd

Hang With Your Buddies to Resist Intersection Attacks

Some anonymity schemes might in principle protect users from pervasive network surveillance - but only if all messages are independent and unlinkable. Users in practice often need pseudonymity - sending messages intentionally linkable to each other but not to the sender - but pseudonymity in dynamic networks exposes users to intersection attacks. We present Buddies, the first systematic design for intersection attack resistance in practical anonymity systems. Buddies groups users dynamically into buddy sets, controlling message transmission to make buddies within a set behaviorally indistinguishable under traffic analysis. To manage the inevitable tradeoffs between anonymity guarantees and communication responsiveness, Buddies enables users to select independent attack mitigation policies for each pseudonym. Using trace-based simulations and a working prototype, we find that Buddies can guarantee non-trivial anonymity set sizes in realistic chat/microblogging scenarios, for both short-lived and long-lived pseudonyms.Comment: 15 pages, 8 figure