Using knowledge graphs to assist deep learning models in making recommendation decisions has recently been proven to effectively improve the model’s interpretability and accuracy. This paper introduces an end-to-end deep learning model, named representation-enhanced knowledge graph convolutional networks (RKGCN), which dynamically analyses each user’s preferences and makes a recommendation of suitable items. It combines knowledge graphs on both the item side and user side to enrich their representations to maximize the utilization of the abundant information in knowledge graphs. RKGCN is able to offer more personalized and relevant recommendations in three different scenarios. The experimental results show the superior effectiveness of our model over 5 baseline models on three real-world datasets including movies, books, and music.

Text classification is widely studied in natural language processing (NLP). Deep learning models, including large pre-trained models like BERT and DistilBERT, have achieved impressive results in text classification tasks. However, these models’ robustness against adversarial attacks remains an area of concern. To address this concern, we propose three data augmentation methods to improve the robustness of such pre-trained models. We evaluated our methods on four text classification datasets by fine-tuning DistilBERT on the augmented datasets and exposing the resulting models to adversarial attacks to evaluate their robustness. In addition to enhancing the robustness, our proposed methods can improve the accuracy and F1-score on three datasets. We also conducted comparison experiments with two existing data augmentation methods. We found that one of our proposed methods demonstrates a similar improvement in terms of performance, but all demonstrate a superior robustness improvement.

This paper improves the performance of the revised-simplified verifiable re-encryption mixnet (R-SVRM) based e-voting scheme by introducing confirmation numbers (CNs) that are used in the CN based e-voting scheme. Although CN based and R-SVRM based schemes had made e-voting schemes more practical by excluding zero knowledge proof (ZKP) that requires large volume of computations, still they are not efficient enough. Namely, the CN based scheme adopts RSA encryption functions that are not probabilistic or commutative, therefore to satisfy essential requirements of elections, extra random factors are necessary for individual votes, election authorities must sign on votes and they must keep encryption keys as their secrets. On the other hand, although the R-SVRM based scheme uses ElGamal encryption functions that are probabilistic and commutative, vote forms in it is complicated, i.e. vote forms consist of at least 3 items and they include information about candidates as exponents. The improved scheme simplifies vote forms by exploiting CNs, and extensively reduces the number of operations required for individual votes. Also, the scheme successfully satisfies all essential requirements of e-voting systems, i.e. it is endowed with features about privacy, robustness, accuracy, incoercibility and fairness as CN based and R-SVRM based schemes are.

In this paper, a novel model for encrypting text messages using time varying delayed Hopfield neural network and a posterior DNA cryptographic model is proposed. The chaotic neural network applied here is used to generate a binary sequence which is later passed to a permutation function and generate the key for the first level encryption. The plaintext is converted to a corresponding binary sequence after a conversion to ASCI value and encrypted by switching of chaotic neural network maps and a permutation function which is dependent on the binary sequence generated from the chaotic neural network. An additional DNA cryptographic model is used over the cipher text obtained from the first level encryption to robust the security of the proposed model.

Selecting representative objects from a large-scale database is an essential task to understand the database. A skyline query is one of the popular methods for selecting representative objects. It retrieves a set of non-dominated objects. In this paper, we consider a distributed algorithm for computing skyline, which is efficient enough to handle “big data”. We have noticed the importance of “big data” and want to use it. On the other hand, we must take care of its privacy. In conventional distributed algorithms for computing a skyline query, we must disclose the sensitive values of each object of a private database to another for comparison. Therefore, the privacy of the objects is not preserved. However, such disclosures of sensitive information in conventional distributed database systems are not allowed in the modern privacy-aware computing environment. Recently several privacy-preserving skyline computation frameworks have been introduced. However, most of them use computationally expensive secure comparison protocol for comparing homomorphically encrypted data. In this work, we propose a novel and efficient approach for computing the skyline in a secure multi-party computing environment without disclosing the individual attributes’ value of the objects. We use a secure multi-party sorting protocol that uses the homomorphic encryption in the semi-honest adversary model for transforming each attribute value of the objects without changing their order on each attribute. To compute skyline we use the order of the objects on each attribute for comparing the dominance relationship among the objects. The security analysis confirms that the proposed framework can achieve multi-party skyline computation without leaking the sensitive attribute value to others. Besides that, our experimental results also validate the effectiveness and scalability of the proposed privacy-preserving skyline computation framework.

Nowadays, the management and analyses of ‘big data’ are becoming indispensable for numerous organizations all over the world. In many cases, multiple organizations want to perform data analyses on their combined databases. Skyline query is one of the popular operations for selecting representative objects from a large database, where any other object within the database does not dominate each of the representative objects, called ‘skyline’. Like other data analytics operations, the multi-party skyline query can provide benefits to the participating organizations by retrieving the skyline objects from their combined databases. Such a multi-party skyline query demands the disclosure of individual parties’ objects to others during the computation. But, owing to the data privacy and security concern of the present IT era, such disclosure of the individual parties’ databases is strictly prohibited. Considering this issue, we are proposing a new framework for the privacy-preserving multi-party skyline query, exploiting additive homomorphic encryption along with data anonymization, perturbation, and randomization techniques. The underlying protocols within our proposed framework ensure that every participating party can identify its multi-party skyline objects without revealing the objects to others during the multi-party skyline query. The detailed privacy and security analyses show that the proposed framework can achieve the desired computation goal without privacy leakage. Besides, the performance evaluation through complexity analyses, extensive simulations, and comprehensive comparison also demonstrate the utility and the efficiency of the proposed framework.