CSI Neural Network: Using Side-channels to Recover Your Artificial Neural Network Information

Machine learning has become mainstream across industries. Numerous examples proved the validity of it for security applications. In this work, we investigate how to reverse engineer a neural network by using only power side-channel information. To this end, we consider a multilayer perceptron as the machine learning architecture of choice and assume a non-invasive and eavesdropping attacker capable of measuring only passive side-channel leakages like power consumption, electromagnetic radiation, and reaction time. We conduct all experiments on real data and common neural net architectures in order to properly assess the applicability and extendability of those attacks. Practical results are shown on an ARM CORTEX-M3 microcontroller. Our experiments show that the side-channel attacker is capable of obtaining the following information: the activation functions used in the architecture, the number of layers and neurons in the layers, the number of output classes, and weights in the neural network. Thus, the attacker can effectively reverse engineer the network using side-channel information. Next, we show that once the attacker has the knowledge about the neural network architecture, he/she could also recover the inputs to the network with only a single-shot measurement. Finally, we discuss several mitigations one could use to thwart such attacks.Comment: 15 pages, 16 figure

Symbolic Side-Channel Analysis for Probabilistic Programs

In this paper we describe symbolic side-channel analysis techniques for detecting and quantifying information leakage, given in terms of Shannon and Min Entropy. Measuring the precise leakage is challenging due to the randomness and noise often present in program executions and side-channel observations. We account for this noise by introducing additional (symbolic) program inputs which are interpreted probabilistically, using symbolic execution with parameterized model counting. We also explore an approximate sampling approach for increased scalability. In contrast to typical Monte Carlo techniques, our approach works by sampling symbolic paths, representing multiple concrete paths, and uses pruning to accelerate computation and guarantee convergence to the optimal results. The key novelty of our approach is to provide bounds on the leakage that are provably under- and over-approximating the real leakage. We implemented the techniques in the Symbolic PathFinder tool and we demonstrate them on Java programs

Language-Based Techniques for Secure Programming

Secure Computation (SC) encompasses many different cryptographic techniques for computing over encrypted data. In particular, Secure Multiparty Computation enables multiple parties to jointly compute a function over their secret inputs. MPC languages offer programmers a familiar environment in which to express their programs, but fall short when confronted with problems that require flexible coordination. More broadly, SC languages do not protect non-expert programmers from violating obliviousness or expected bounds on information leakage. We aim to show that secure programming can be made safer through language-based techniques for expressive, coordinated MPC; probabilistically oblivious execution; and quantitative analysis of information flow. We begin by presenting Symphony, an expressive MPC language that provides flexible coordination of many parties, which has been used to implement the secure shuffle of Laur, Willemson, and Zhang. Next, we present λObliv, a core language guaranteeing that well-typed programs are probabilistically oblivious, which has been used to type check tree-based, nonrecursive ORAM (NORAM). Finally, we present a novel application of dynamic analysis techniques to an existing system for enforcing bounds on information leakage, providing a better balance of precision and performance