Biblio

Mobile code distribution relies on digital signatures to guarantee code authenticity. Unfortunately, standard signature schemes are not well suited for use in conjunction with program transformation techniques, such as aspect-oriented programming. With these techniques, code development is performed in sequence by multiple teams of programmers. This is fundamentally different from traditional single-developer/ single-user models, where users can verify end-to-end (i.e., developer-to-user) authenticity of the code using digital signatures. To address this limitation, we introduce FLEX, a flexible code authentication framework for mobile applications. FLEX allows semi-trusted intermediaries to modify mobile code without invalidating the developer's signature, as long as the modification complies with a "contract" issued by the developer. We introduce formal definitions for secure code modification, and show that our instantiation of FLEX is secure under these definitions. Although FLEX can be instantiated using any language, we design AMJ–a novel programming language that supports code annotations–and implement a FLEX prototype based on our new language.

The low-level C++ programming language is ubiquitously used for its modularity and performance. Typecasting is a fundamental concept in C++ (and object-oriented programming in general) to convert a pointer from one object type into another. However, downcasting (converting a base class pointer to a derived class pointer) has critical security implications due to potentially different object memory layouts. Due to missing type safety in C++, a downcasted pointer can violate a programmer's intended pointer semantics, allowing an attacker to corrupt the underlying memory in a type-unsafe fashion. This vulnerability class is receiving increasing attention and is known as type confusion (or bad-casting). Several existing approaches detect different forms of type confusion, but these solutions are severely limited due to both high run-time performance overhead and low detection coverage. This paper presents TypeSan, a practical type-confusion detector which provides both low run-time overhead and high detection coverage. Despite improving the coverage of state-of-the-art techniques, TypeSan significantly reduces the type-confusion detection overhead compared to other solutions. TypeSan relies on an efficient per-object metadata storage service based on a compact memory shadowing scheme. Our scheme treats all the memory objects (i.e., globals, stack, heap) uniformly to eliminate extra checks on the fast path and relies on a variable compression ratio to minimize run-time performance and memory overhead. Our experimental results confirm that TypeSan is practical, even when explicitly checking almost all the relevant typecasts in a given C++ program. Compared to the state of the art, TypeSan yields orders of magnitude higher coverage at 4–10 times lower performance overhead on SPEC and 2 times on Firefox. As a result, our solution offers superior protection and is suitable for deployment in production software. Moreover, our highly efficient metadata storage back-end is potentially useful for other defenses that require memory object tracking.

Abundant multimedia data generated in our daily life has intrigued a variety of very important and useful real-world applications such as object detection and recognition etc. Accompany with these applications, many popular feature descriptors have been developed, e.g., SIFT, SURF and HOG. Manipulating massive multimedia data locally, however, is a storage and computation intensive task, especially for resource-constrained clients. In this work, we focus on exploring how to securely outsource the famous feature extraction algorithm–Histogram of Oriented Gradients (HOG) to untrusted cloud servers, without revealing the data owner's private information. For the first time, we investigate this secure outsourcing computation problem under two different models and accordingly propose two novel privacy-preserving HOG outsourcing protocols, by efficiently encrypting image data by somewhat homomorphic encryption (SHE) integrated with single-instruction multiple-data (SIMD), designing a new batched secure comparison protocol, and carefully redesigning every step of HOG to adapt it to the ciphertext domain. Explicit Security and effectiveness analysis are presented to show that our protocols are practically-secure and can approximate well the performance of the original HOG executed in the plaintext domain. Our extensive experimental evaluations further demonstrate that our solutions achieve high efficiency and perform comparably to the original HOG when being applied to human detection.

Databases have become one of the most important components in modern software systems. For example, web services, cloud computing systems, and online transaction processing systems all rely heavily on databases. To abstract the complexity of accessing a database, developers make use of Object-Relational Mapping (ORM) frameworks. ORM frameworks provide an abstraction layer between the application logic and the underlying database. Such abstraction layer automatically maps objects in Object-Oriented Languages to database records, which significantly reduces the amount of boilerplate code that needs to be written. Despite the advantages of using ORM frameworks, we observe several difficulties in maintaining ORM code (i.e., code that makes use of ORM frameworks) when cooperating with our industrial partner. After conducting studies on other open source systems, we find that such difficulties are common in other Java systems. Our study finds that i) ORM cannot completely encapsulate database accesses in objects or abstract the underlying database technology, thus may cause ORM code changes more scattered; ii) ORM code changes are more frequent than regular code, but there is a lack of tools that help developers verify ORM code at compilation time; iii) we find that changes to ORM code are more commonly due to performance or security reasons; however, traditional static code analyzers need to be extended to capture the peculiarities of ORM code in order to detect such problems. Our study highlights the hidden maintenance costs of using ORM frameworks, and provides some initial insights about potential approaches to help maintain ORM code. Future studies should carefully examine ORM code, especially given the rising use of ORM in modern software systems.

An Enterprise Content Management (ECM) system must withstand many queries to its access control subsystem in order to check permissions in support of browsing-oriented operations. This leads us to choose a subject-oriented representation for access control (i.e., maintaining a permissions list for each subject). Additionally, if identifiers (OIDs) are assigned to objects in a breadth-first traversal of the object hierarchy, we will encounter many contiguous OIDs when browsing under one object (e.g., folder). Based on these observations, we present a space-efficient data structure specifically tailored for representing permissions lists in ECM systems. In addition to achieving space efficiency, the operations to check, grant, or revoke a permission are very fast using our data structure. Furthermore, our design supports fast union and intersection of two or more permissions lists (determining the effective permissions inherited from several users' groups or the common permissions among sets of users). Finally, the data structure is scalable to support any increase in the number of objects and subjects. We evaluate our design by comparing it against a compressed (WAH) bitmap-based representation and a hashing-based representation, using both synthetic and real-world data under both random and breadth-first OID numbering schemes.

Mobile devices, such as smarthphones, became a common tool in our daily routine. Mobile Applications (a.k.a. apps) are demanding access to contextual information increasingly. For instance, apps require user's environment data as well as their profiles in order to adapt themselves (interfaces, services, content) according to this context data. Mobile apps with this behavior are known as context-aware applications (CAS). Several software infrastructures have been created to help the development of CAS. However, most of them do not store the contextual data, once mobile devices are resource constrained. They are not built taking into account the privacy of contextual data either, due the fact that apps may expose contextual data, without user consent. This paper addresses these topics by extending an existing middleware platform that help the development of mobile context-aware applications. Our extension aims at store and process the contextual data generated from several mobile devices, using the computational power of the cloud, and the definition of privacy policies, which avoid dissemination of unauthorized contextual data.

Internet Service Providers (ISPs) use the Border Gateway Protocol (BGP) to announce and exchange routes for de- livering packets through the internet. ISPs must carefully configure their BGP routers to ensure traffic is routed reli- ably and securely. Correctly configuring BGP routers has proven challenging in practice, and misconfiguration has led to worldwide outages and traffic hijacks. This paper presents Bagpipe, a system that enables ISPs to declaratively express BGP policies and that automatically verifies that router configurations implement such policies. The novel initial network reduction soundly reduces policy verification to a search for counterexamples in a finite space. An SMT-based symbolic execution engine performs this search efficiently. Bagpipe reduces the size of its search space using predicate abstraction and parallelizes its search using symbolic variable hoisting. Bagpipe's policy specification language is expressive: we expressed policies inferred from real AS configurations, policies from the literature, and policies for 10 Juniper TechLibrary configuration scenarios. Bagpipe is efficient: we ran it on three ASes with a total of over 240,000 lines of Cisco and Juniper BGP configuration. Bagpipe is effective: it revealed 19 policy violations without issuing any false positives.