Mashups are a prevailing kind of web applications integrating external gadget APIs often written in the JavaScript programming language. Writing secure mashups is a challenging task due to the heterogeneity of existing gadget APIs, the privileges granted to gadgets during mashup executions, and JavaScript’s highly dynamic environment. We propose a new compiler, called Mashic, for the automatic generation of secure JavaScript-based mashups from existing mashup code. The Mashic compiler can effortlessly be applied to existing mashups based on a wide-range of gadget APIs. It offers security and correctness guarantees. Security is achieved via the Same Origin Policy. Correctness is ensured in the presence of benign gadgets, that satisfy confidentiality and integrity constraints with regard to the integrator code. The compiler has been successfully applied to real world mashups based on Google maps, Bing maps, YouTube, and Zwibbler APIs.

We propose a new compiler, called Mashic, for the automatic generation of secure Javascript-based mashups from existing mashup code. The Mashic compiler can effortlessly be applied to existing mashups based on a wide-range of gadget APIs. It offers security and correctness guarantees. Security is achieved via the Same Origin Policy. Correctness is ensured in the presence of benign gadgets, that satisfy confidentiality and integrity constrains with regard to the integrator code. The compiler has been successfully applied to real world mashups based on Google maps, Bing maps, YouTube, and Zwibbler APIs.

We propose a small-step operational semantics to support reasoning about Web applications written in the multitier language HOP. The semantics covers both server side and client side computations, as well as their interactions, and includes creation of Web services, distributed client-server communications, concurrent evaluation of service requests at server side, elaboration of HTML documents, DOM operations, evaluation of script nodes in HTML documents and actions from HTML pages at client side. We also model the browser same origin policy (SOP) in the semantics. We propose a safety property by which programs do not get stuck due to a violation of the SOP and a type system to enforce it.

Anonymous communication protocols must achieve two seemingly contradictory goals: privacy (informally, they must guarantee the anonymity of the parties that send/receive information), and robustness (informally, they must ensure that the messages are not tampered). However, the long line of research that defines and analyzes the security of such mechanisms focuses almost exclusively on the former property and ignores the latter. In this paper, we initiate a rigorous study of robustness properties for anonymity protocols. We identify and formally define, using the style of modern cryptography, two related but distinct flavors of robustness. Our definitions are general (e.g. they strictly generalize the few existent notions for particular protocols) and flexible (e.g. they can be easily adapted to purely combinatorial/probabilistic mechanisms). We demonstrate the use of our definitions through the analysis of several anonymity mechanisms (Crowds, broadcast-based mix-nets, DC-nets, Tor). Notably, we analyze the robustness of a protocol by Golle and Juels for the dining cryptographers problem, identify a robustness-related weakness of the protocol, and propose and analyze a stronger version.

The population protocol model [2] has emerged as an elegant computation paradigm for describing mobile ad hoc networks, consisting of a number of mobile nodes that interact with each other to carry out a computation. The interactions of nodes are subject to a fairness constraint. One essential property of population protocols is that all nodes must eventually converge to the correct output value (or configuration). In this paper, we aim to automatically verify self-stabilizing population protocols for leader election and token circulation in the Spin model checker [8]. We report our verification results and discuss the issue of modeling strong fairness constraints in spin.