Mapping Computations in Heterogeneous Multicore Systems with Statistical Regression on Program Inputs

A hardware configuration is a set of processors and their frequency levels in a multicore heterogeneous system. This article presents a compiler-based technique to match functions with hardware configurations. Such a technique consists of using multivariate linear regression to associate function arguments with particular hardware configurations. By showing that this classification space tends to be convex in practice, this article demonstrates that linear regression is not only an efficient tool to map computations to heterogeneous hardware, but also an effective one. To demonstrate the viability of multivariate linear regression as a way to perform adaptive compilation for heterogeneous architectures, we have implemented our ideas onto the Soot Java bytecode analyzer. Code that we produce can predict the best configuration for a large class of Java and Scala benchmarks running on an Odroid XU4 big.LITTLE board; hence, outperforming prior techniques such as ARM’s GTS and CHOAMP, a recently released static program scheduler.

Java Bytecode Control Flow Classification: Framework for Guiding Java Decompilation

Decompilation is the main process of software development, which is very important when a program tries to retrieve lost source codes. Although decompiling Java bytecode is easier than bytecode, many Java decompilers cannot recover originally lost sources, especially the selection statement, i.e., if statement. This deficiency affects directly decompilation performance. In this paper, we propose the methodology for guiding Java decompiler to deal with the aforementioned problem. In the framework, Java bytecode is transformed into two kinds of features called frame feature and latent semantic feature. The former is extracted directly from the bytecode. The latter is achieved by two-step transforming the Java bytecode to bigram and then term frequency-inverse document frequency (TFIDF). After that, both of them are fed to the genetic algorithm to reduce their dimensions. The proposed feature is achieved by converting the selected TFIDF to a latent semantic feature and concatenating it with the selected frame feature. Finally, KNN is used to classify the proposed feature. The experimental results show that the decompilation accuracy is 93.68 percent, which is obviously better than Java Decompiler.

JDart: Dynamic Symbolic Execution for Java Bytecode (Competition Contribution)

JDart performs dynamic symbolic execution of Java programs: it executes programs with concrete inputs while recording symbolic constraints on executed program paths. A constraint solver is then used for generating new concrete values from recorded constraints that drive execution along previously unexplored paths. JDart is built on top of the Java PathFinder software model checker and uses the JConstraints library for the integration of constraint solvers.

Java Ranger at SV-COMP 2020 (Competition Contribution)

Path-merging is a known technique for accelerating symbolic execution. One technique, named “veritesting” by Avgerinos et al. uses summaries of bounded control-flow regions and has been shown to accelerate symbolic execution of binary code. But, when applied to symbolic execution of Java code, veritesting needs to be extended to summarize dynamically dispatched methods and exceptional control-flow. Such an extension of veritesting has been implemented in Java Ranger by implementing as an extension of Symbolic PathFinder, a symbolic executor for Java bytecode. In this paper, we briefly describe the architecture of Java Ranger and describe its setup for SV-COMP 2020.

Computation on Encrypted Data using Dataflow Authentication

Encrypting data before sending it to the cloud protects it against attackers, but requires the cloud to compute on encrypted data. Trusted modules, such as SGX enclaves, promise to provide a secure environment in which data can be decrypted and then processed. However, vulnerabilities in the executed program, which becomes part of the trusted code base (TCB), give attackers ample opportunity to execute arbitrary code inside the enclave. This code can modify the dataflow of the program and leak secrets via SGX side-channels. Since any larger code base is rife with vulnerabilities, it is not a good idea to outsource entire programs to SGX enclaves. A secure alternative relying solely on cryptography would be fully homomorphic encryption. However, due to its high computational complexity it is unlikely to be adopted in the near future. Researchers have made several proposals for transforming programs to perform encrypted computations on less powerful encryption schemes. Yet current approaches do not support programs making control-flow decisions based on encrypted data.We introduce the concept of dataflow authentication (DFAuth) to enable such programs. DFAuth prevents an adversary from arbitrarily deviating from the dataflow of a program. Our technique hence offers protections against the side-channel attacks described above. We implemented DFAuth using a novel authenticated homomorphic encryption scheme, a Java bytecode-tobytecode compiler producing fully executable programs, and an SGX enclave running a small and program-independent TCB. We applied DFAuth to an existing neural network that performs machine learning on sensitive medical data. The transformation yields a neural network with encrypted weights, which can be evaluated on encrypted inputs in 0.86 s.