Most social platforms offer mechanisms allowing users to delete their posts, and a significant fraction of users exercise this right to be forgotten. However, ironically, users' attempt to reduce attention to sensitive posts via deletion, in practice, attracts unwanted attention from stalkers specifically to those posts. Thus, deletions may leave users more vulnerable to attacks on their privacy in general. Users hoping to make their posts forgotten face a "damned if I do, damned if I don't" dilemma. Many are shifting towards ephemeral social platform like Snapchat, which will deprive us of important user-data archival. In the form of intermittent withdrawals, we present, Lethe, a novel solution to this problem of forgetting the forgotten. If the next-generation social platforms are willing to give up the uninterrupted availability of non-deleted posts by a very small fraction, Lethe provides privacy to the deleted posts over long durations. In presence of Lethe, an adversarial observer becomes unsure if some posts are permanently deleted or just temporarily withdrawn by Lethe; at the same time, the adversarial observer is overwhelmed by a large number of falsely flagged undeleted posts. To demonstrate the feasibility and performance of Lethe, we analyze large-scale real data about users' deletion over Twitter and thoroughly investigate how to choose time duration distributions for alternating between temporary withdrawals and resurrections of non-deleted posts. We find a favorable trade-off between privacy, availability and adversarial overhead in different settings for users exercising their right to delete. We show that, even against an ultimate adversary with an uninterrupted access to the entire platform, Lethe offers deletion privacy for up to 3 months from the time of deletion, while maintaining content availability as high as 95 adversarial precision to 20
We combine collective signing and blockchains to create a secure and easy-to-use, decentralized SSH-key management system.
In this paper, we present a rigorous methodology for quantifying the anonymity provided by Tor against a variety of structural attacks, i.e., adversaries that corrupt Tor nodes and thereby perform eavesdropping attacks to deanonymize Tor users. First, we provide an algorithmic approach for computing the anonymity impact of such structural attacks against Tor. The algorithm is parametric in the considered path selection algorithm and is, hence, capable of reasoning about variants of Tor and alternative path selection algorithms as well. Second, we present formalizations of various instantiations of structural attacks against Tor and show that the computed anonymity impact of each of these adversaries indeed constitutes a worst-case anonymity bound for the cryptographic realization of Tor. Third, we use our methodology to conduct a rigorous, largescale evaluation of Tor’s anonymity which establishes worst-case anonymity bounds against various structural attacks for Tor and for alternative path selection algorithms such as DistribuTor, SelekTOR, and LASTor. This yields the first rigorous anonymity comparison between different path selection algorithms. As part of our analysis, we quantify the anonymity impact of a path selection transition phase, i.e., a small number of users decides to run an alternative algorithm while the vast majority still uses the original one. The source code of our implementation is publicly available.
Given current research initiatives advocating “clean slate” Internet designs, researchers have the opportunity to design an internetwork layer routing protocol that provides efficient anonymity by decoupling identity from network location. Prior work in anonymity for the next-generation Internet fully trusts the user’s ISP. We propose Dovetail, which 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 applicationlayer proxy in data transmission. We address this 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. We develop a systematic mechanism to measure the topological anonymity of our designs, and we demonstrate their privacy and efficiency by Internet-scale simulations at the AS-level.
Revelations of large scale electronic surveillance and data mining by governments and corporations have fueled increased adoption of HTTPS. We present a traffic analysis attack against over 6000 webpages spanning the HTTPS deployments of 10 widely used, industry-leading websites in areas such as healthcare, finance, legal services and streaming video. Our attack identifies individual pages in the same website with 90% accuracy, exposing personal details including medical conditions, financial and legal affairs and sexual orientation. We examine evaluation methodology and reveal accuracy variations as large as 17% caused by assumptions affecting caching and cookies. We present a novel defense reducing attack accuracy to 25% with a 9% traffic increase, and demonstrate significantly increased effectiveness of prior defenses in our evaluation context, inclusive of enabled caching, user-specific cookies and pages within the same website.
