Nicolae Constantinescu

The development of information security mechanisms follows a cyclic refinement model: new cryptographic solutions are proposed, their limitations are studied, and improvements are introduced to overcome those limitations. This paper contributes to this process by proposing a blind signature scheme with tunable anonymity, adapted to application-specific requirements. The core of the model is a parameter T that allows the user to balance confidentiality with transparency from the settings and adapt the signature behavior to the particular requirements of a beneficiary. Compared to the models currently used in blind signature protocols, this approach offers improved resistance to brute force attacks and improves security against adaptive and man-in-the-middle threats. Due to the reduced computational power requirements needed to calculate cryptographic primitives, it is usable for devices with power constraints. Being able to be integrated into a blockchain infrastructure, the process supports both persistent and verifiable data records, which gives it flexibility to adapt to different types of decentralized platforms.

Association rule mining plays a critical role in uncovering item correlations and hidden patterns within transactional data, particularly in e-commerce environments. Despite the widespread use of Apriori and FP-Growth algorithms, few studies offer a statistically rigorous, tool-based comparison of their performance on real-world e-commerce data. This paper addresses this gap by evaluating both algorithms in terms of execution time, memory consumption, rule generation volume, and rule strength (support, confidence, and lift). Implementations in RapidMiner and an analysis through SPSS establish statistically significant performance differences, particularly under varying support thresholds. Our findings confirm that FP-Growth consistently outperforms Apriori for large-scale datasets due to its ability to bypass candidate generation, while Apriori retains pedagogical and small-scale relevance. The study contributes practical guidance for data scientists and e-commerce practitioners choosing suitable rule-mining techniques based on their data size and performance constraints.

Computing systems grouped in subnets use distributed security models, in general, by creating session keys based on the Diffie–Hellman model, and calculating the necessary parameters for this, on each of the systems. In the particular case of a network of devices heterogeneous in terms of computing power, such as IoT, the modeling of a security system of the entire structure will have to take into account the fact that some devices have a very low computing power. In this sense, starting from the study of some general models, used in structures of this type, an integrated structure was developed to secure communications and test certain vulnerable components, to calculate a degree of risk that they are maliciously intended. The system was developed with a customized mathematical model, a scheme for propagation and management of cryptographic parameters and a test in a real environment by creating the algorithmic model and implementing it within a structure of a beneficiary.

This study proposes a hierarchical communication framework for drone swarms designed to enhance security and operational efficiency. Leveraging elliptic curve cryptography and space quanta concepts, the model ensures continuous authentication and risk assessment of participating entities. Experimental results demonstrate the framework’s effectiveness in mitigating security risks, achieving reliable communication even in adverse conditions. Key findings include significant improvement in threat detection accuracy and reduced computational overhead, validating the model’s applicability for real-world drone swarm operations. These contributions establish a robust foundation for secure and resilient drone coordination.

The detection of clear and encrypted data that are transported through computer networks is of particular importance both for protecting the data and the users to whom they belong and to whom they are intended, as well as the networks through which they are transmitted. The proposed method consists of an algorithm that classifies the data it receives by testing the belongingness of their standard deviation values to established confidence intervals. Following the evaluation of the algorithm, an accuracy of 94.73% was obtained and it appears that the results can be used with certainty in subsequent analyses of the data detection.

Mathematical models based on elliptic curves have been intensively studied since their applicability in data security systems was discovered. In this article, the authors describe the optimal way to select particular subspaces over which elliptic curves are defined, showing the applicability of these subspaces in secure data transfer. Access to large databases and analyses of the requests made to these databases are required daily by a variety of users, including legal entities. An attack on these communication systems causes violations in privacy and damage to/theft of data that can be worth EUR tens of billions annually. For requests made between computers, encryption methods can be used as these systems have adequate computing power and energy. For requests made from fixed and mobile systems, if the data are distributed heterogeneously, the computing power required to authenticate both the users and the answering entities determines the efficiency of the proposed solution. To address this limitation, our study proposes a double-authentication method based on particular elliptic-curve systems.