The rapid increase in global censorship events has stimulated a substantial growth in users relying on circumvention tools. Fighting against censors requires tool maintainers to frequently update client-side configurations and proxy IPs. However, existing methods for doing so require clients to explicitly query for updates. Further, this client-initiated communication relies mostly on ad-hoc and out-of-band channels. This work demonstrates the utility of push notification services as an efficient and sustainable communication channel between tool maintainers and their clients. A push notification channel allows tool maintainers to update client configurations automatically without the need for clients to initiate a query themselves. We develop a general-purpose design for integrating push notifications as a control channel in circumvention tools. We utilize the design to integrate and implement push notifications for use in the popular circumvention tool Tor and demonstrate their utility to push bridge line updates to Tor clients.

Tor, one of the most popular censorship circumvention systems, faces regular blocking attempts by censors. Thus, to facilitate access, it relies on "pluggable transports" (PTs) that disguise Tor's traffic and make it hard for the adversary to block Tor. However, these are not yet well studied and compared for the performance they provide to the users. Thus, we conduct a first comparative performance evaluation of a total of 12 PTs-the ones currently supported by the Tor project and those that can be integrated in the future.

Traditional censorship revolves around blocking access to some websites (or services) over the Internet. However, recently there has been a rise in the events of an extreme form of censorship viz., deliberate Internet shutdown, leading to complete Internet disconnection, severely impacting lives in such regions. Naturally, these shutdowns render all existing circumvention schemes unusable. Thus, we present Dolphin, a first of its kind system that can provide access to lightweight and delay tolerant Internet applications (email, tweets, news snippets, etc.) during Internet shutdowns. Dolphin uses the cellular voice channel to transmit data bits. A user in the shutdown region (who wishes to access these applications) requires a peer in non-shutdown region to send and retrieve content on its behalf. The data bits between the peers are sent by first encoding them into audio and then transmitting them over a cellular voice call. We overcome multiple challenges while designing and implementing Dolphin. E.g., the cellular voice channel is inherently lossy and unreliable. But the Internet applications need reliable transfers. Thus, in Dolphin we develop a TCP-style reliability layer to overcome the losses that works atop any underlying encoding and modulation scheme. Further, to evade eavesdroppers over the insecure voice channel, we provide end-to-end confidentiality. Also, Dolphin can function even without human intervention, by using cellular voice automation services. We experimentally show that Dolphin works for Internet applications, by testing it for sending email, tweets and accessing news snippets. All these applications take a few minutes to be accessed (e.g., a 500 character email was received in under 2 minutes).

Free and open communication over the Internet is considered a fundamental human right, essential to prevent repressions from silencing voices of dissent. This has led to the development of various anti-censorship systems. Recent systems have relied on a common blocking resistance strategy i.e., incurring collateral damage to the censoring regimes, if they attempt to restrict such systems. However, despite being promising, systems built on such strategies pose additional challenges, viz., deployment limitations, poor QoS etc. These challenges prevent their wide scale adoption.

Anonymous VoIP calls over the Internet holds great significance for privacy-conscious users, whistle-blowers and political activists alike. Prior research deems popular anonymization systems like Tor unsuitable for providing the requisite performance guarantees that real-time applications like VoIP need. Their claims are backed by studies that may no longer be valid due to constant advancements in Tor. Moreover, we believe that these studies lacked the requisite diversity and comprehensiveness. Thus, conclusions from these studies, led them to propose novel and tailored solutions. However, no such system is available for immediate use. Additionally, operating such new systems would incur significant costs for recruiting users and volunteered relays, to provide the necessary anonymity guarantees.

Abstract Decoy Routing (DR), a promising approach to censorship circumvention, uses routers (rather than end hosts) as proxy servers. Users of censored networks, who wish to use DR, send specially crafted packets, nominally addressed to an uncensored website. Once safely out of the censored network, the packets encounter a special router (the Decoy Router) which identifies them using a secret handshake, and proxies them to their true destination (a censored site). However, DR has implementation problems: it is infeasible to reprogram routers for the complex operations required. Existing DR solutions fall back on using commodity servers as a Decoy Router. But as servers are not efficient at routing, most web applications show poor performance when accessed over DR. A further concern is that the Decoy Router has to inspect all flows in order to identify the ones that need DR. This may itself be a breach of privacy for other users (who neither require DR nor want to be monitored). In this paper, we present a novel DR system, Siege- Breaker (SB), which solves the aforementioned problems using an SDN-based architecture. Previous proposals involve a single unit which performs all major operations (inspecting all flows, identifying the DR requests and proxying them). In contrast, SB distributes the tasks for DR among three independent modules. (1) The SDN controller identifies DR requests via a covert, privacy preserving scheme, and does not need to inspect all flows. (2) The reconfigurable SDN switch intercepts packets, and forwards them to a secret proxy efficiently. (3) The secret proxy server proxies the client’s traffic to the censored site. Our modular, lightweight design achieves performance comparable to direct TCP downloads, for both in-lab setups, and Internet based tests involving commercial SDN switches.

Jensen-Shannon divergence (JSD) does not provide adequate separation when the difference between input distributions is subtle. A recently introduced technique, Chisini Jensen Shannon Divergence (CJSD), increases JSD's ability to discriminate between probability distributions by reformulating with operators from Chisini mean. As a consequence, CJSDs also carry additional properties concerning robustness. The utility of this approach was validated in the form of two SVM kernels that give superior classification performance. Our work explores why the performance improvement to JSDs is afforded by this reformulation. We characterize the nature of this improvement based on the idea of relative dilation, that is how Chisini mean transforms JSD's range and prove a number of propositions that establish the degree of this separation. Finally, we provide empirical validation on a synthetic dataset that confirms our theoretical results pertaining to relative dilation.

Jensen-Shannon divergence is an effective method for measuring the distance between two probability distributions. When the difference between these two distributions is subtle, Jensen-Shannon divergence does not provide adequate separation to draw distinctions from subtly different distributions. We extend Jensen-Shannon divergence by reformulating it using alternate operators that provide different properties concerning robustness. Furthermore, we prove a number of important properties for this extension: the lower limits of its range, and its relationship to Shannon Entropy and Kullback-Leibler divergence. Finally, we propose a family of new kernels, based on Chisini mean Jensen-Shannon divergence, and demonstrate its utility in providing better SVM classification accuracy over RBF kernels for amino acid spectra. Because spectral methods capture phenomenon at subatomic levels, differences between complex compounds can often be subtle. While the impetus behind this work began with spectral data, the methods are generally applicable to domains where subtle differences are important.