Secure (n, n + 1)-Multi Secret Image Sharing Scheme Using Additive Modulo

AbstractMulti Secret Image Sharing (MSIS) scheme is a protected method to transmit more than one secret images over a communication channel. Conventionally, only single secret image is shared over a channel at a time. But as technology grew up, there arises a need for sharing more than one secret image. An (n, n)-MSIS scheme is used to encrypt n secret images into n meaningless noisy images that are stored over different servers. To recover n secret images all n noise images are required. At earlier time, the main problem with secret sharing schemes was that one can partially figure out secret images by getting access of n – 1 or fewer noisy images. Due to this, there arises a need of secure MSIS scheme so that by using less than n noisy images no information can be retrieved. In this paper, we propose secure (n, n + 1)-MSIS scheme using additive modulo operation for grayscale and colored images. The experimental results show that the proposed scheme is highly secured and altering of noisy images will not reveal any partial information about secret images. The proposed (n, n + 1)-MSIS scheme outperforms the existing MSIS schemes in terms of security

DeepSecure: Scalable Provably-Secure Deep Learning

This paper proposes DeepSecure, a novel framework that enables scalable execution of the state-of-the-art Deep Learning (DL) models in a privacy-preserving setting. DeepSecure targets scenarios in which neither of the involved parties including the cloud servers that hold the DL model parameters or the delegating clients who own the data is willing to reveal their information. Our framework is the first to empower accurate and scalable DL analysis of data generated by distributed clients without sacrificing the security to maintain efficiency. The secure DL computation in DeepSecure is performed using Yao's Garbled Circuit (GC) protocol. We devise GC-optimized realization of various components used in DL. Our optimized implementation achieves more than 58-fold higher throughput per sample compared with the best-known prior solution. In addition to our optimized GC realization, we introduce a set of novel low-overhead pre-processing techniques which further reduce the GC overall runtime in the context of deep learning. Extensive evaluations of various DL applications demonstrate up to two orders-of-magnitude additional runtime improvement achieved as a result of our pre-processing methodology. This paper also provides mechanisms to securely delegate GC computations to a third party in constrained embedded settings

Secret Sharing in Visual Cryptography

This thesis examines techniques for recursive hiding scheme for 3 out of 5 secret sharing and a probabilistic 2 out of 3 secret sharing scheme for gray scale images. In recursive hiding of secrets several messages can be hidden in one of the shares of the original secret image. The images that are to be hidden are taken according to their sizes from smaller to the largest. The first small secret image is divided into five different shares using visual cryptography. These shares are placed in the next level to create the shares of larger secret information. The shares at each consecutive level are distributed so that no one has access to all the shares of the smaller images, unless at least three participants come together to reveal the secret information, resulting in 3 out of 5 scheme. In the proposed protocol for gray scale images, the quality of the image is perfect when it is reconstructed for the construction of the final image based on the binary OR operation.Computer Science Departmen

A Reversible Steganography Scheme of Secret Image Sharing Based on Cellular Automata and Least Significant Bits Construction

Secret image sharing schemes have been extensively studied by far. However, there are just a few schemes that can restore both the secret image and the cover image losslessly. These schemes have one or more defects in the following aspects: (1) high computation cost; (2) overflow issue existing when modulus operation is used to restore the cover image and the secret image; (3) part of the cover image being severely modified and the stego images having worse visual quality. In this paper, we combine the methods of least significant bits construction (LSBC) and dynamic embedding with one-dimensional cellular automata to propose a new lossless scheme which solves the above issues and can resist differential attack and support parallel computing. Experimental results also show that this scheme has the merit of big embedding capacity

Efficient Random Grid Visual Cryptographic Schemes having Essential Members

In this paper we consider ``OR based monochrome random grid visual cryptographic schemes (RGVCS) for $t$-$(k,n)^*$ access structure which is a generalization of the threshold $(k,n)$ access structure in the sense that in all the successful attempts to recover the secret image, the $t$ essential participants must always be present. Up to the best of our knowledge, the current proposed work is the first in the literature of RGVCS which provides efficient direct constructions for the $t$-$(k,n)^*$-RGVCS for ``OR based model. Finding the closed form of light contrast is a challenging work. However, in this paper we come up with the closed form of the light contrast for the ``OR based model. In literature, there are visual cryptographic schemes where the secret reconstruction is done by binary ``XOR operation instead of ``OR operation to increase the relative contrast of the decoded image. In this paper, we also propose an extended grid based $t$-$(k,n)^*$-RGVCS in which we replace the traditional ``OR operation by ``XOR operation. Note that the use of XOR operation indicates that the decoding must be performed computationally and not visually. We justified our schemes using both experimental as well as simulation based data

Information-Theoretic Secure Outsourced Computation in Distributed Systems

Secure multi-party computation (secure MPC) has been established as the de facto paradigm for protecting privacy in distributed computation. One of the earliest secure MPC primitives is the Shamir\u27s secret sharing (SSS) scheme. SSS has many advantages over other popular secure MPC primitives like garbled circuits (GC) -- it provides information-theoretic security guarantee, requires no complex long-integer operations, and often leads to more efficient protocols. Nonetheless, SSS receives less attention in the signal processing community because SSS requires a larger number of honest participants, making it prone to collusion attacks. In this dissertation, I propose an agent-based computing framework using SSS to protect privacy in distributed signal processing. There are three main contributions to this dissertation. First, the proposed computing framework is shown to be significantly more efficient than GC. Second, a novel game-theoretical framework is proposed to analyze different types of collusion attacks. Third, using the proposed game-theoretical framework, specific mechanism designs are developed to deter collusion attacks in a fully distributed manner. Specifically, for a collusion attack with known detectors, I analyze it as games between secret owners and show that the attack can be effectively deterred by an explicit retaliation mechanism. For a general attack without detectors, I expand the scope of the game to include the computing agents and provide deterrence through deceptive collusion requests. The correctness and privacy of the protocols are proved under a covert adversarial model. Our experimental results demonstrate the efficiency of SSS-based protocols and the validity of our mechanism design

Advances in Grid Computing

This book approaches the grid computing with a perspective on the latest achievements in the field, providing an insight into the current research trends and advances, and presenting a large range of innovative research papers. The topics covered in this book include resource and data management, grid architectures and development, and grid-enabled applications. New ideas employing heuristic methods from swarm intelligence or genetic algorithm and quantum encryption are considered in order to explain two main aspects of grid computing: resource management and data management. The book addresses also some aspects of grid computing that regard architecture and development, and includes a diverse range of applications for grid computing, including possible human grid computing system, simulation of the fusion reaction, ubiquitous healthcare service provisioning and complex water systems