Cloud application marketplaces of modern cloud infrastructures offer a new software deployment model, integrated with the cloud environment in its configuration and policies. However, similar to traditional software distribution which has been suffering from software piracy and reverse engineering, cloud marketplaces face the same challenges that can deter the success of the evolving ecosystem of cloud software. We present a novel system named CAFE for cloud infrastructures where sensitive software logic can be executed with high secrecy protected from any piracy or reverse engineering attempts in a virtual machine even when its operating system kernel is compromised. The key mechanism is the end-to-end framework for the execution of applications, which consists of the secure encryption and distribution of confidential application binary files, and the runtime techniques to load, decrypt, and protect the program logic by isolating them from tenant virtual machines based on hypervisor-level techniques. We evaluate applications in several software categories which are commonly offered in cloud marketplaces showing that strong confidential execution can be provided with only marginal changes (around 100-220 lines of code) and minimal performance overhead. The results demonstrate the effectiveness and practicality of CAFE in cloud marketplaces.

Cloud application marketplaces of modern cloud infrastructures offer a new software deployment model, integrated with the cloud environment in its configuration and policies. However, similar to traditional software distribution which has been suffering from software piracy and reverse engineering, cloud marketplaces face the same challenges that can deter the success of the evolving ecosystem of cloud software. We present a novel system named CAFE for cloud infrastructures where sensitive software logic can be executed with high secrecy protected from any piracy or reverse engineering attempts in a virtual machine even when its operating system kernel is compromised. The key mechanism is the end-to-end framework for the execution of applications, which consists of the secure encryption and distribution of confidential application binary files, and the runtime techniques to load, decrypt, and protect the program logic by isolating them from tenant virtual machines based on hypervisor-level techniques. We evaluate applications in several software categories which are commonly offered in cloud marketplaces showing that strong confidential execution can be provided with only marginal changes (around 100-220 lines of code) and minimal performance overhead.

In imperative synchronous languages, a statement is called schizophrenic if it is executed more than once in a single clock. When a schizophrenic statement is translated into a circuit, the circuit can behave abnormally because of the multiple executions. To solve the problems caused by schizophrenic statements, compilers duplicate the statements to avoid multiple executions. Esterel is an imperative synchronous language. Schizophrenic statements in Esterel are considered to occur due to the instantaneous reentrance of local signal declarations or parallel statements. However, if the corresponding circuit of a schizophrenic statement behaves normally, it is harmless and thus curing is not necessary. In this paper, we identify the conditions under which a schizophrenic statement of the Esterel program must be cured during circuit translation. We also propose an algorithm to detect schizophrenic statements that have to be cured on the control flow graphs (CFGs) of source codes. Our algorithm detects all schizophrenic statements that have to be cured and results in fewer false alarms on the benchmark programs used in the previous work. It is simple and based on the CFG of a program, implying that it can be merged into existing compilers easily.

A software birthmark means inherent characteristics that can be used to identify a program. In this paper, we propose a birthmark technique based on object traces of Java programs. Java is an object-oriented programming language that provides various predefined class libraries that help programmers to produce software easily. In order to utilize Java class libraries, we have to use Java object instructions. The Java object instructions are hard to replace or remove, and so a set of sequences of object instructions is a proper candidate to represent inherent characteristics of a program. We propose a new birthmark using the sequences of object instructions. We evaluate the proposed birthmark with open source programs and compare it with previous static birthmarks. Experiments show that the detection capability of our birthmark is much higher than that of other static birthmarks despite obfuscations by Smokescreen and ZKM.

Esterel is an imperative synchronous language for control-dominant reactive systems. The combination of imperative structures and the perfect synchrony hypothesis often result in schizophrenic statements. Previous studies explain the characteristics of schizophrenia as the instantaneous reentrance to block statements: local signal declarations and parallel statements. In practice, however, most instantly-reentered block statements do not cause any problems in Esterel compilation. In this paper, we refine schizophrenic problems in terms of signal emissions, and suggest an algorithm to detect harmful schizophrenia using reachability on control flow graphs (CFGs) in Esterel. Our algorithm performs well in analyzing practical programs. Moreover, it can be easily applied to existing compilers.

One proposal for automatic construction of proofs about programs is to combine Hoare logic and abstract interpretation. Constructing proofs is in Hoare logic. Discovering programs' invariants is done by abstract interpreters. One problem of this approach is that abstract interpreters often compute invariants that are not needed for the proof goal. The reason is that the abstract interpreter does not know what the proof goal is, so it simply tries to find as strong invariants as possible. These unnecessary invariants increase the size of the constructed proofs. Unless the proof-construction phase is notified which invariants are not needed, it blindly proves all the computed invariants. In this article, we present a framework for designing algorithms, called abstract-value slicers , that slice out unnecessary invariants from the results of forward abstract interpretation. The framework provides a generic abstract-value slicer that can be instantiated into a slicer for a particular abstract interpretation. Such an instantiated abstract-value slicer works as a post-processor to an abstract interpretation in the whole proof-construction process, and notifies to the next proof-construction phase which invariants it does not have to prove. Using the framework, we designed an abstract-value slicer for an existing relational analysis and applied it on programs. In this experiment, the slicer identified 62%--81% of the computed invariants as unnecessary, and resulted in 52%--84% reduction in the size of constructed proofs.

The inversion of a neural network is a process of computing inputs that produce a given target when fed into the neural network. The inversion algorithm of crisp neural networks is based on the gradient descent search in which a candidate inverse is iteratively refined to decrease the error between its output and the target. In this paper. we derive an inversion algorithm of fuzzified neural networks from that of crisp neural networks. First, we present a framework of learning algorithms of fuzzified neural networks and introduce the idea of adjusting schemes for fuzzy variables. Next, we derive the inversion algorithm of fuzzified neural networks by applying the adjusting scheme for fuzzy variables to total inputs in the input layer. Finally, we make three experiments on the parity-three problem, examine the effect of the size of training sets on the inversion, and investigate how the fuzziness of inputs and targets of training sets affects the inversion.

Path expressions have been accepted for concisely manipulating the nested structures in complex object-oriented query expressions. However, previous visual query languages hardly represent such query expressions in a concise and intuitive way partly due to improper visual representation of path expressions and partly due to lack of well-defined system and semantics of languages. In this paper, we present visual modeling of path expressions in a visual object-oriented database query language called Visual Object-Oriented Query Language (VOQL) which has excellent expressive pourer for sets, simple and intuitive syntax, and well-defined semantics. This is enabled by explicitly specifying the semantics of multi-valued path expressions based on the visual notation capable of representing set relationships in addition to functional relationships. The basic visual constructs called blobs and nested blobs denote sets of objects that path expressions represent while the constructs called binding edges and flattening edges visually simulate the notions of variable binding and dot functions in path expressions respectively. Based on the constructs, the grammar of VOQL defines the syntactic components while the semantics of query expressions are provided by syntax-directed translation to the counterparts in the extended relational calculus. Also, the visual constructs allow modeling of restricted universal quantification with a visual scoping box and effectively represent nested quantification and recursive queries without semantic ambiguities.