Using Data Masking for Balancing Security and Performance in Data Warehousing (original) (raw)
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ARTIFICIAL NEURAL CRYPTOGRAPHY DATAGRAM HIDING TECHNIQUES FOR COMPUTER SECURITY OBJECTS REGISTER
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2021 IEEE 39th International Conference on Computer Design (ICCD), 2021
Numerous applications such as graph processing, cryptography, databases, bioinformatics, etc., involve the repeated evaluation of Boolean functions on large bit vectors. In-memory architectures which perform processing in memory (PIM) are tailored for such applications. This paper describes a different architecture for in-memory computation called CIDAN, that achieves a 3X improvement in performance and a 2X improvement in energy for a representative set of algorithms over the state-of-the-art in-memory architectures. CIDAN uses a new basic processing element called a TLPE, which comprises a threshold logic gate (TLG) (a.k.a artificial neuron or perceptron). The implementation of a TLG within a TLPE is equivalent to a multi-input, edge-triggered flipflop that computes a subset of threshold functions of its inputs. The specific threshold function is selected on each cycle by enabling/disabling a subset of the weights associated with the threshold function, by using logic signals. In addition to the TLG, a TLPE realizes some non-threshold functions by a sequence of TLG evaluations. An equivalent CMOS implementation of a TLPE requires a substantially higher area and power. CIDAN has an array of TLPE(s) that is integrated with a DRAM, to allow fast evaluation of any one of its set of functions on large bit vectors. Results of running several common in-memory applications in graph processing and cryptography are presented.
Bidirectional Associative Memory Neural Network for Data Encryption and Decryption
International Journal for Research in Applied Science and Engineering Technology, 2018
Successful encryption and decryption of data has been a prime concern for any data transfer application. Data may be unwantedly attacked by a malicious attacker. Usually during data transfer, data in encrypted form along with a key is transferred, if a malicious attacker is able to get the key and is able to figure out the encryption algorithm used, the data no longer remains safe. Making a data encryption/decryption system, where there in no need to feed the encrypted data along with the key during data transfer, where key dynamically changes, can make our system more resistant to attackers. Block ciphers with symmetric key encryption are a well know technique for data encryption. A neuro block cipher can be established using recurrent neural networks to develop an encryption system with a dynamically changing key. Neuro recurrent networks can be researched to provide a new edge to network security.
Computational properties of use to biological organisms or to the construction of computers can emerge as collective properties of systems -having a large number of simple equivalent components (or neurons). The physical meaning ofcontent-addressable memory is described by an appropriate phase space flow of the state of a system. A model of such a system is given, based on aspects of neurobiology but readily adapted to integrated circuits. The collective properties of this model produce a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size. The algorithm for the time evolution of the state of the system is based on asynchronous parallel processing. Additional emergent collective properties include some capacity for generalization, familiarity recognition, categorization, error correction, and time sequence retention. The collective properties are only weakly sensitive to details ofthe modeling or the failure of individual devices.
Neural Network as a Programmable Block Cipher
Advances in Information Processing and Protection, 2007
A model of Boolean neural network is proposed as a substitute of a bock cipher. Such a network has functionality of the block cipher and one additional advantage: it can change its cryptographic properties without reprogramming, by training the network with a new training set. The constriction of the network is presented with an analysis of the applied binary transformations. Also three methods of training the network (what corresponds to the re-keying of a block cipher) are presented. Their security and effectiveness are analyzed and compared.
Information Hiding is a method of hiding secret messages into a cover-media such that an intended observer will not be aware of the existence of hidden messages. In such applications various file formats are used as cover-object which contains confidential data. As the new research field the techniques introduces Artificial Neural Network. It is an efficient method to solve complex problems. This newly developed technique uses Multilayer Perceptron algorithm of Neural Network for data security. Results are observed through different Media file as cover-objects. In the proposed method, MLP algorithm is implemented with traditional substitution method to obtain high embedding capacity with no visibility. The system provides confidentiality and integrity to the data during communication through open channels.
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International Journal of Computer Applications, 2013
In this paper we are studying the tolerance of Hopfield neural network for storage and recalling of fingerprint images. The feature extraction of these images is performed with FFT, DWT and SOM. These feature vectors are stored as associative memory in Hopfield Neural Network with Hebbian learning and Pseudoinverse learning rules. The objective of this study is to determine the optimal weight matrix for efficient recalling of the memorized pattern for the presented noisy or distorted and incomplete prototype patterns from the Hopfield network. This study also explores the tolerance in Hopfield neural network for reducing the effect of false minimas in the recalling process. Besides this the capabilities of learning rules for pattern storage is also analyzed. This study also exhibits the analysis as pattern storage networks for feature vectors obtained from SOM with FFT and DWT
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Applied Physics A, 2011
Increasing density based on bit size reduction is currently a main driving force for the development of data storage technologies. However, it is expected that all of the current available storage technologies might approach their physical limits in around 15 to 20 years due to miniaturization. To further advance the storage technologies, it is required to explore a new development trend that is different from density driven. One possible direction is to derive insights from biological counterparts. Unlike physical memories that have a single function of data storage, human memory is versatile. It contributes to functions of data storage, information processing, and most importantly, cognitive functions such as adaptation, learning, perception, knowledge generation, etc. In this paper, a brief review of current data storage technologies are presented, followed by discussions of future storage technology development trend. We expect that the driving force will evolve from density to functionality, and new memory modules associated with additional functions other than only data storage will appear. As an initial step toward building a future generation memory technology, we propose Artificial Cognitive Memory (ACM), a memory based intelligent system. We also present the characteristics of ACM, new technologies that can be used to develop ACM components such as bioinspired element cells (silicon, memristor, phase change, etc.), and possible methodologies to construct a biologically inspired hierarchical system.