We present PIDGIN, a program analysis and understanding tool that enables the specification and enforcement of precise applicationspecific information security guarantees. PIDGIN also allows developers to interactively explore the information flows in their applications to develop policies and investigate counter-examples.PIDGIN combines program dependence graphs (PDGs), which precisely capture the information flows in a whole application, with a custom PDG query language. Queries express properties about the paths in the PDG; because paths in the PDG correspond to information flows in the application, queries can be used to specify global security policies.PIDGIN is scalable. Generating a PDG for a 330k line Java application takes 90 seconds, and checking a policy on that PDG takes under 14 seconds. The query language is expressive, supporting a large class of precise, application-specific security guarantees. Policies are separate from the code and do not interfere with testing or development, and can be used for security regression testing.We describe the design and implementation of PIDGIN and report on using it: (1) to explore information security guarantees in legacy programs; (2) to develop and modify security policies concurrently with application development; and (3) to develop policies based on known vulnerabilities.

Fabric is a new system and language for building secure distributed information systems. It is a decentralized system that allows heterogeneous network nodes to securely share both information and computation resources despite mutual distrust. Its high-level programming language makes distribution and persistence largely transparent to programmers. Fabric supports data-shipping and functionshipping styles of computation: both computation and information can move between nodes to meet security requirements or to improve performance. Fabric provides a rich, Java-like object model, but data resources are labeled with confidentiality and integrity policies that are enforced through a combination of compile-time and run-time mechanisms. Optimistic, nested transactions ensure consistency across all objects and nodes. A peer-to-peer dissemination layer helps to increase availability and to balance load. Results from applications built using Fabric suggest that Fabric has a clean, concise programming model, offers good performance, and enforces security.

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We present Clio, an information flow control (IFC) system that transparently incorporates cryptography to enforce confidentiality and integrity policies on untrusted storage. Clio insulates developers from explicitly manipulating keys and cryptographic primitives by leveraging the policy language of the IFC system to automatically use the appropriate keys and correct cryptographic operations. We prove that Clio is secure with a novel proof technique that is based on a proof style from cryptography together with standard programming languages results. We present a prototype Clio implementation and a case study that demonstrates Clio's practicality.

Modern service-oriented applications forgo semantically rich protocols and middleware when composing services. Instead, they embrace the loosely-coupled development and deployment of services that communicate via simple network protocols. Even though these applications do expose interfaces that are higher-order in spirit, the simplicity of the network protocols forces them to rely on brittle low-level encodings. To bridge the apparent semantic gap, programmers introduce ad-hoc and error-prone defensive code. Inspired by Design by Contract, we choose a different route to bridge this gap. We introduce Whip, a contract system for modern services. Whip (i) provides programmers with a higher-order contract language tailored to the needs of modern services; and (ii) monitors services at run time to detect services that do not live up to their advertised interfaces. Contract monitoring is local to a service. Services are treated as black boxes, allowing heterogeneous implementation languages without modification to services' code. Thus, Whip does not disturb the loosely coupled nature of modern services. CCS Concepts: • Information systems → Service discovery and interfaces; • Software and its engineering → Domain specific languages; Interface definition languages;

Abstract. Disjunction Category Labels (DC-labels) are an expressive label format used to classify the sensitivity of data in information-flow control systems. DC-labels use capability-like privileges to downgrade information. Inappropriate use of privileges can compromise security, but DC-labels provide no mechanism to ensure appropriate use. We extend DC-labels with the novel notions of bounded privileges and robust privileges. Bounded privileges specify and enforce upper and lower bounds on the labels of data that may be downgraded. Bounded privileges are simple and intuitive, yet can express a rich set of desirable security policies. Robust privileges can be used only in downgrading operations that are robust, i.e., the code exercising privileges cannot be abused to release or certify more information than intended. Surprisingly, robust downgrades can be expressed in DC-labels as downgrading operations using a weakened privilege. We provide sound and complete run-time security checks to ensure downgrading operations are robust. We illustrate the applicability of bounded and robust privileges in a case study as well as by identifying a vulnerability in an existing DC-label-based application.

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Research on differential privacy is generally concerned with examining data sets that are static. Because the data sets do not change, every computation on them produces "one-shot" query results; the results do not change aside from randomness introduced for privacy . There are many circumstances, however, where this model does not apply, or is simply infeasible. Data streams are examples of non-static data sets where results may change as more data is streamed. Theoretical support for differential privacy with data streams has been researched in the form of differentially private streaming algorithms. In this paper, we present a practical framework for which a non-expert can perform differentially private operations on data streams. The system is built as an extension to PINQ (Privacy Integrated Queries), a differentially private programming framework for static data sets. The streaming extension provides a programmatic interface for the different types of streaming differential privacy from the literature so that the privacy trade-offs of each type of algorithm can be understood by a non-expert programmer.