Search engine queries contain a great deal of private and potentially compromising information about users. One technique to prevent search engines from identifying the source of a query, and Internet service providers (ISPs) from identifying the contents of queries is to query the search engine over an anonymous network such as Tor. In this paper, we study the extent to which Website Fingerprinting can be extended to fingerprint individual queries or keywords to web applications, a task we call Keyword Fingerprinting (KF). We show that by augmenting traffic analysis using a two-stage approach with new task-specific feature sets, a passive network adversary can in many cases defeat the use of Tor to protect search engine queries. We explore three popular search engines, Google, Bing, and Duckduckgo, and several machine learning techniques with various experimental scenarios. Our experimental results show that KF can identify Google queries containing one of 300 targeted keywords with recall of 80% and precision of 91%, while identifying the specific monitored keyword among 300 search keywords with accuracy 48%. We also further investigate the factors that contribute to keyword fingerprintability to understand how search engines and users might protect against KF.
Social media websites are blocked in many regimes where Internet censorship is applied. In this paper, we introduce Mailet, an unobservable transport proxy which enables the users to access social websites by email applications. Without assuming the Mailet servers are trustworthy, Mailet can support the services requiring privileges without having the complete credential. Particularly, the credential is split and distributed in two Mailet servers, and neither of them can recover the credential alone. To recover the credential in a TLS record message, we propose a highly efficient Galois/Counter Mode(GCM) based secure computation, which can enable the two servers to conceal their separate credential copies in the computation. We implemented a prototype for Twitter.com to demonstrate the usability and security of Mailet.
Beetle antennae search (BAS) is an efficient metaheuristic algorithm inspired by foraging behaviors of beetles. This algorithm includes several parameters for tuning and the existing results are limited to solve single objective optimization. This work pushes forward the research on BAS by providing one variant that releases the tuning parameters and is able to handle multi-objective optimization. This new approach applies normalization to simplify the original algorithm and uses a penalty function to exploit infeasible solutions with low constraint violation to solve the constraint optimization problem. Extensive experimental studies are carried out and the results reveal efficacy of the proposed approach to constraint handling.
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