Lightweight and Secure PUF Key Storage Using Limits of Machine Learning

13th International Workshop, Nara, Japan, September 28 – October 1, 2011. ProceedingsA lightweight and secure key storage scheme using silicon Physical Unclonable Functions (PUFs) is described. To derive stable PUF bits from chip manufacturing variations, a lightweight error correction code (ECC) encoder / decoder is used. With a register count of 69, this codec core does not use any traditional error correction techniques and is 75% smaller than a previous provably secure implementation, and yet achieves robust environmental performance in 65nm FPGA and 0.13μ ASIC implementations. The security of the syndrome bits uses a new security argument that relies on what cannot be learned from a machine learning perspective. The number of Leaked Bits is determined for each Syndrome Word, reducible using Syndrome Distribution Shaping. The design is secure from a min-entropy standpoint against a machine-learning-equipped adversary that, given a ceiling of leaked bits, has a classification error bounded by ε. Numerical examples are given using latest machine learning results

Deep Reinforcement Learning: An Overview

In recent years, a specific machine learning method called deep learning has gained huge attraction, as it has obtained astonishing results in broad applications such as pattern recognition, speech recognition, computer vision, and natural language processing. Recent research has also been shown that deep learning techniques can be combined with reinforcement learning methods to learn useful representations for the problems with high dimensional raw data input. This chapter reviews the recent advances in deep reinforcement learning with a focus on the most used deep architectures such as autoencoders, convolutional neural networks and recurrent neural networks which have successfully been come together with the reinforcement learning framework.Comment: Proceedings of SAI Intelligent Systems Conference (IntelliSys) 201

Phosphorylation and calcium binding properties of an Arabidopsis GF14 brain protein homolog.

Arabidopsis GF14 omega was originally described because of its apparent association with a DNA-protein complex; it is a member of the 14-3-3 kinase regulatory protein family that is conserved throughout eukaryotes. Here, we demonstrated that recombinant GF14 omega is expressed in Escherichia coli as a dimer. Blot binding and electrophoretic mobility shift analyses indicated that GF14 omega binds calcium. Equilibrium dialysis further demonstrated that GF14 omega binds an equimolar amount of calcium with an apparent binding constant of 5.5 x 10(4) M-1 under physiological conditions. The C-terminal domain, which contains a potential EF hand motif, is responsible for the calcium binding. The C-terminal domain also cross-reacted with the anti-GF14 omega monoclonal antibody. In addition, GF14 omega is phosphorylated by Arabidopsis protein kinase activity at a serine residue(s) in vitro. Therefore, GF14 omega protein has biochemical properties consistent with potential signaling roles in plants. The presence of a potential EF hand-like motif in the highly conserved C terminus of 14-3-3 proteins together with the calcium-dependent multiple functions attributed to the 14-3-3 proteins indicate that the C terminus EF hand is a common functional element of this family of proteins

Specific interactions with TBP and TFIIB in vitro suggest that 14-3-3 proteins may participate in the regulation of transcription when part of a DNA binding complex.

The 14-3-3 family of multifunctional proteins is highly conserved among animals, plants, and yeast. Several studies have shown that these proteins are associated with a G-box DNA binding complex and are present in the nucleus in several plant and animal species. In this study, 14-3-3 proteins are shown to bind the TATA box binding protein (TBP), transcription factor IIB (TFIIB), and the human TBP-associated factor hTAF(II)32 in vitro but not hTAF(II)55. The interactions with TBP and TFIIB were highly specific, requiring amino acid residues in the box 1 domain of the 14-3-3 protein. These interactions do not require formation of the 14-3-3 dimer and are not dependent on known 14-3-3 recognition motifs containing phosphoserine. The 14-3-3-TFIIB interaction appears to occur within the same domain of TFIIB that binds the human herpes simplex virus transcriptional activator VP16, because VP16 and 14-3-3 were able to compete for interaction with TFIIB in vitro. In a plant transient expression system, 14-3-3 was able to activate GAL4-dependent beta-glucuronidase reporter gene expression at low levels when translationally fused with the GAL4 DNA binding domain. The in vitro binding with general transcription factors TBP and TFIIB together with its nuclear location provide evidence supporting a role for 14-3-3 proteins as transcriptional activators or coactivators when part of a DNA binding complex

Plasma membrane H+-ATPase and 14-3-3 Isoforms of Arabidopsis leaves: Evidence for isoform specificity in the 14-3-3/H+-ATPase interaction

The plasma membrane H+-ATPase is activated by binding of 14-3-3 protein to the phosphorylated C terminus. Considering the large number of 14-3-3 and H+-ATPase isoforms in Arabidopsis (13 and 11 expressed genes, respectively), specificity in binding may exist between 14-3-3 and H+-ATPase isoforms. We now show that the H'-ATPase is the main target for 14-3-3 binding at the plasma membrane, and that all twelve 14-3-3 istiforms tested bind to the H+-ATPase in vitro. Using specific antibodies for nine of the 14-3-3 isoforms, we show that GF14epsilon, mu, lambda, omega, chi, phi, nu, and upsilon are present in leaves, but that isolated plasma membranes lack GF14chi, phi and upsilon. Northern blots using isoform-specific probes for all 14-3-3 and H+-ATPase isoforms showed that transcripts were present for most of the isoforms. Based on mRNA levels, GF14epsilon, mu, lambda and chi are highly expressed 14-3-3 isoforms, and AHA1, 3, and 11 highly expressed H+-ATPase isoforms in leaves. However, mass peptide fingerprinting identified AHA1 and 2 with the highest score, and their presence could be confirmed by MS/MS. It may be calculated that under 'unstressed' conditions less than one percent of total 14-3-3 is attached to the H+-ATPase. However, during a condition requiring full activation of H+ pumping, as induced here by the presence of the fungal toxin fusicoccin, several percent of total 14-3-3 may be engaged in activation of the H+-ATPase

Reinforcement Learning

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