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Public-key (or asymmetric key) algorithms are the most dominant cryptographic algorithms in the cryptography era. RSA, in particular, is a good example of algorithms that belong to this category which is used for many applications and its security is beyond doubt. The key size of RSA is an important factor that determines its robustness. As the computational powers evolve, the need to increase the security of cryptosystems becomes essential to achieve more robustness against cryptanalysis attacks. In the literature, the commonly used solution to achieve this goal is to increase the key size. However, in practice, keeping increasing the key size is not feasible and not unlimited; increasing the key size demands more computational power. The increase in computational power makes it hard (or impossible) to conduct the encryption process in some environments such as smart cards. Also, it should be taken into consideration that the dramatic increase in computational capabilities of new machines improves their abilities to break encryption keys. Therefore, an alternative to increasing the key size is essentially needed. In this paper, we propose a new approach for encryption that is based on RSA main algorithm while using more encryption keys with smaller sizes in addition to extra security information component (known as the Security Card, SeCa). Our results show that the use of multiple-shorter keys with the SeCa component produces significant improvements in the performance of RSA algorithm in terms of increasing security and reducing the computation overhead by decreasing encryption and decryption times. General TermsSecurity, Algorithms, Encryption.

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Understanding user’s behavior and their interactions with artificial-intelligent-based systems is as important as analyzing the performance of the algorithms used in these systems. For instance, in the Recommender Systems domain, the accuracy of the recommendation algorithm was the ultimate goal for most systems designers. However, researchers and practitioners have realized that providing accurate recommendations is insufficient to enhance users’ acceptance. A recommender system needs to focus on other factors that enhance its interactions with the users. Recent researches suggest augmenting these systems with persuasive capabilities. Persuasive features lead to increasing users’ acceptance of the recommendations, which, in turn, enhances users’ experience with these systems. Nonetheless, the literature still lacks a comprehensive view of the actual effect of persuasive principles on recommender users. To fill this gap, this study diagnoses how users of different characteristics get influenced by various persuasive principles that a recommender system uses. The study considers four users’ aspects: age, gender, culture (continent), and personality traits. The paper also investigates the impact of the context (or application domain) on the influence of the persuasive principles. Two application domains (namely eCommerce and Movie recommendations) are considered. A within-subject user study was conducted. The analysis of (279) responses revealed that persuasive principles have the potential to enhance users’ experience with recommender systems. The study also shows that, among the considered factors, culture, personality traits, and the domain of recommendations have a higher impact on the influence of persuasive principles than other factors. Based on the analysis of the results, the study provides insights and guidelines for recommender systems designers. These guidelines can be used as a reference for designing recommender systems with users’ experience in mind. We suggest that considering the results presented in this paper could help to improve recommender-users interaction.

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