PRIVACY PRESERVATION FOR TRANSACTION INITIATORS: STRONGER KEY IMAGE RING SIGNATURE AND SMART CONTRACT-BASED FRAMEWORK

Recently, blockchain technology has garnered support. However, an attenuating factor to its global adoption in certain use cases is privacy-preservation owing to its inherent transparency. A widely explored cryptographic option to address this challenge has been ring signature which aside its privacy guarantee must be double spending resistant. In this paper, we identify and prove a catastrophic flaw for double-spending attack in a Lightweight Ring Signature scheme and proceed to construct a new, fortified commitment scheme using the signer’s entire private key. Subsequently, we compute a stronger key image to yield a double-spending-resistant signature scheme solidly backed by formal proof. Inherent in our solution is a novel, zero-knowledge-based, secured and cost-effective smart contract for public key aggregation. We test our solution on a private blockchain as well as Kovan testnet along with performance analysis attesting to efficiency and usability and make the code publicly available on GitHub

Privacy preservation for transaction initiators: stronger key image ring signature and smart contract-based framework

Recently, blockchain technology has garnered a great deal of support; however, an attenuating factor to its global adoption in certain use cases is privacypreservation (owing to its inherent transparency). A widely explored cryptographic option to address this challenge has been a ring signature that, aside from its privacy guarantee, must be double-spending resistant. In this paper, we identify and prove a catastrophic flaw for double-spending attacks in a lightweight ring signature scheme and proceed to construct a new fortified commitment scheme that uses a signer’s entire private key. Subsequently, we compute a stronger key image to yield a double-spending-resistant signature scheme that is solidly backed by formal proof. Inherent in our solution is a novel, zero-knowledge-based, secure, and cost-effective smart contract for public key aggregation. We test our solution on a private blockchain as well as a Kovan testnet along with a performance analysis that attests to its efficiency and usability – and, we make the code publicly available on GitHub

Forensic detection of heterogeneous activity in data using deep learning methods

The abundance of digital images has been facilitated by smartphones and inexpensive storage. Digital forensic investigation requires the processing of tons of digital images collected on devices to either identify or validate the device's user or to ascertain whether the operator has any connections to the case that would be of interest. Examining and evaluating heterogeneous activity presents several difficulties, including variability, complex interaction across information, and volume. Digital forensics processes are said to need the inspection and analysis stages. This research presents a hybrid optimization of the Grey Wolf and artificial bee colony (GW-ABC) optimization with deep learning model Convolutional Neural Network (CNN) i.e., GW-ABC-CNN, and the developed framework is integrated as a module for Autopsy software. The main objective of this research is to detect the heterogeneous activity of humans from the Heterogeneous Human Activity Recognition (HHAR) database. The developed model is integrated into the data-source ingest module; in this module, pre-processing, feature extraction, and detection process is performed. Moreover, in the pre-processing stage, the Min-Max normalization method is used and the required frequency and time features are extracted using the GW-ABC method. In addition, CNN is used to detect heterogeneous activity; this detection process is performed by four layers. Finally, the effectiveness of the developed model is assessed, and the outcomes of using the GW-ABC-CNN paradigm were compared to those of other strategies to evaluate the model's effectiveness