Inherent Weight Normalization in Stochastic Neural Networks

Multiplicative stochasticity such as Dropout improves the robustness and generalizability of deep neural networks. Here, we further demonstrate that always-on multiplicative stochasticity combined with simple threshold neurons are sufficient operations for deep neural networks. We call such models Neural Sampling Machines (NSM). We find that the probability of activation of the NSM exhibits a self-normalizing property that mirrors Weight Normalization, a previously studied mechanism that fulfills many of the features of Batch Normalization in an online fashion. The normalization of activities during training speeds up convergence by preventing internal covariate shift caused by changes in the input distribution. The always-on stochasticity of the NSM confers the following advantages: the network is identical in the inference and learning phases, making the NSM suitable for online learning, it can exploit stochasticity inherent to a physical substrate such as analog non-volatile memories for in-memory computing, and it is suitable for Monte Carlo sampling, while requiring almost exclusively addition and comparison operations. We demonstrate NSMs on standard classification benchmarks (MNIST and CIFAR) and event-based classification benchmarks (N-MNIST and DVS Gestures). Our results show that NSMs perform comparably or better than conventional artificial neural networks with the same architecture

Study on the effect of toxicity under highly arsenic prone zone in Nadia district of West Bengal in India

The present study was carried out on the basis of status of arsenic in soil, drinking water and plants, blood, urine and faeces of animals at arsenic prone zone. Within the ambit with the environment, the examination of animals was taken into consideration. They were screened and categorised on the degree of As toxicity. For field works animals were randomly selected from arsenic prone zone. The external manifestation indicated a complex syndrome and characteristic signs such as increased heart rate and respiratory rate, red urine, congested mucous membrane, anorexia, absence of ruminal motility, diarrhoea with blood, polyuria and unusual weight loss. The haematobiochemical changes such as low Hb level, decreased level of TEC, TLC and increased level ALT, AST, BUN and creatinine. Increased level of arsenic in urine, blood and faeces than the value of control animals could be the confirmatory indication of arsenic toxicity

Functional Signcryption: Notion, Construction, and Applications

Functional encryption (FE) enables sophisticated control over decryption rights in a multi-user scenario, while functional signature (FS) allows to enforce complex constraints on signing capabilities. This paper introduces the concept of functional signcryption (FSC) that aims to provide the functionalities of both FE and FS in an unified cost-effective primitive. FSC provides a solution to the problem of achieving confidentiality and authenticity simultaneously in digital communication and storage systems involving multiple users with better efficiency compared to a sequential implementation of FE and FS. We begin by providing formal definition of FSC and formulating its security requirements. Next, we present a generic construction of this challenging primitive that supports arbitrary polynomial-size signing and decryption functions from known cryptographic building blocks, namely, indistinguishability obfuscation (IO) and statistically simulation-sound noninteractive zero-knowledge proof of knowledge (SSS-NIZKPoK). Finally, we exhibit a number of representative applications of FSC: (I) We develop the first construction of attribute-based signcryption (ABSC) supporting signing and decryption policies representable by general polynomial-size circuits from FSC. (II) We show how FSC can serve as a tool for building SSS-NIZKPoK system and IO, a result which in conjunction with our generic FSC construction can also be interpreted as establishing an equivalence between FSC and the other two fundamental cryptographic primitives

Succinct Predicate and Online-Offline Multi-Input Inner Product Encryptions under Standard Static Assumptions

This paper presents expressive predicate encryption (PE) systems, namely non-zero inner-product-predicate encryption (NIPPE) and attribute-based encryption (ABE) supporting monotone span programs achieving best known parameters among existing similar schemes under well-studied static complexity assumptions. Both the constructions are built in composite order bilinear group setting and involve only 2 group elements in the ciphertexts. More interestingly, our NIPPE scheme, which additionally features only 1 group element in the decryption keys, is the first to attain succinct ciphertexts and decryption keys simultaneously. For proving selective security of these constructions under the Subgroup Decision assumptions, which are the most standard static assumptions in composite order bilinear group setting, we apply the extended version of the elegant D´ej`a Q framework, which was originally proposed as a general technique for reducing the q-type complexity assumptions to their static counter parts. Our work thus demonstrates the power of this framework in overcoming the need of q-type assumptions, which are vulnerable to serious practical attacks, for deriving security of highly expressive PE systems with compact parameters. We further introduce the concept of online-offline multi-input functional encryption (OO-MIFE), which is a crucial advancement towards realizing this highly promising but computationally intensive cryptographic primitive in resource bounded and power constrained devices. We also instantiate our notion of OO-MIFE by constructing such a scheme for the multi-input analog of the inner product functionality, which has a wide range of application in practice. Our OO-MIFE scheme for multiinput inner products is built in asymmetric bilinear groups of prime order and is proven selectively secure under the well-studied k-Linear (k-LIN) assumption

Neural Sampling Machine with Stochastic Synapse allows Brain-like Learning and Inference

Many real-world mission-critical applications require continual online learning from noisy data and real-time decision making with a defined confidence level. Probabilistic models and stochastic neural networks can explicitly handle uncertainty in data and allow adaptive learning-on-the-fly, but their implementation in a low-power substrate remains a challenge. Here, we introduce a novel hardware fabric that implements a new class of stochastic NN called Neural-Sampling-Machine that exploits stochasticity in synaptic connections for approximate Bayesian inference. Harnessing the inherent non-linearities and stochasticity occurring at the atomic level in emerging materials and devices allows us to capture the synaptic stochasticity occurring at the molecular level in biological synapses. We experimentally demonstrate in-silico hybrid stochastic synapse by pairing a ferroelectric field-effect transistor -based analog weight cell with a two-terminal stochastic selector element. Such a stochastic synapse can be integrated within the well-established crossbar array architecture for compute-in-memory. We experimentally show that the inherent stochastic switching of the selector element between the insulator and metallic state introduces a multiplicative stochastic noise within the synapses of NSM that samples the conductance states of the FeFET, both during learning and inference. We perform network-level simulations to highlight the salient automatic weight normalization feature introduced by the stochastic synapses of the NSM that paves the way for continual online learning without any offline Batch Normalization. We also showcase the Bayesian inferencing capability introduced by the stochastic synapse during inference mode, thus accounting for uncertainty in data. We report 98.25%accuracy on standard image classification task as well as estimation of data uncertainty in rotated samples

Adaptively Secure Unrestricted Attribute-Based Encryption with Subset Difference Revocation in Bilinear Groups of Prime Order

Providing an efficient revocation mechanism for attribute-based encryption (ABE) is of utmost importance since over time a user’s credentials may be revealed or expired. All previously known revocable ABE (RABE) constructions (a) essentially utilize the complete subtree (CS) scheme for revocation purpose, (b) are bounded in the sense that the size of the public parameters depends linearly on the size of the attribute universe and logarithmically on the number of users in the system, and (c) are either selectively secure, which seems unrealistic in a dynamic system such as RABE, or adaptively secure but built in a composite order bilinear group setting, which is undesirable from the point of view of both efficiency and security. This paper presents the first adaptively secure unbounded RABE using subset difference (SD) mechanism for revocation which greatly improves the broadcast efficiency compared to the CS scheme. Our RABE scheme is built on a prime order bilinear group setting resulting in practical computation cost, and its security depends on the Decisional Linear assumption

Functional Encryption for Inner Product with Full Function Privacy

Functional encryption (FE) supports constrained decryption keys that allow decrypters to learn specific functions of encrypted messages. In numerous practical applications of FE, confidentiality must be assured not only for the encrypted data but also for the functions for which functional keys are provided. This paper presents a non-generic simple private key FE scheme for the inner product functionality, also known as inner product encryption (IPE). In contrast to the existing similar schemes, our construction achieves the strongest indistinguishability-based notion of function privacy in the private key setting without employing any computationally expensive cryptographic tool or non-standard complexity assumption. Our construction is built in the asymmetric bilinear pairing group setting of prime order. The security of our scheme is based on the well-studied Symmetric External Diffie-Hellman (SXDH) assumption

Verifiable and Delegatable Constrained Pseudorandom Functions for Unconstrained Inputs

Constrained pseudorandom functions (CPRF) are a fundamental extension of the notion of traditional pseudorandom functions (PRF). A CPRF enables a master PRF key holder to issue constrained keys corresponding to specific constraint predicates over the input domain. A constrained key can be used to evaluate the PRF only on those inputs which are accepted by the associated constraint predicate. However, the PRF outputs on the rest of the inputs still remain computationally indistinguishable from uniformly random values. A constrained verifiable pseudorandom function (CVPRF) enhances a CPRF with a non-interactive public verification mechanism for checking the correctness of PRF evaluations. A delegatable constrained pseudorandom function (DCPRF) is another extension which augments a CPRF to empower constrained key holders to delegate further constrained keys that allow PRF evaluations on inputs accepted by more restricted constraint predicates compared to ones embedded in their own constrained keys. Until recently, all the proposed constructions of CPRF’s and their extensions(i) either could handle only bounded length inputs, (ii) or were based on risky knowledge-type assumptions. In EUROCRYPT 2016, Deshpande et al. have presented a CPRF construction supporting inputs of unconstrained polynomial length based on indistinguishability obfuscation and injective pseudorandom generators, which they have claimed to be selectively secure. In this paper, we first identify a flaw in their security argument and resolve this by carefully modifying their construction and suitably redesigning the security proof. Our alteration does not involve any additional heavy duty cryptographic tools. Next, employing only standard public key encryption (PKE), we extend our CPRF construction, presenting the first ever CVPRF and DCPRF constructions that can handle inputs of unbounded polynomial length. Finally, we apply our ideas to demonstrate the first known attribute-based signature (ABS) scheme for general signing policies supporting signing attributes of arbitrary polynomial length