One of the most interesting topics in the scope of social network analysis is dynamic community detection, keeping track of communities' evolutions in a dynamic network. This article introduces a new Louvain-based dynamic community detection algorithm relied on the derived knowledge of the previous steps of the network evolution. The algorithm builds a compressed graph, where its supernodes represent the detected communities of the previous step and its superedges show the edges among the supernodes. The algorithm not only constructs the compressed graph with low computational complexity but also detects the communities through the integration of the Louvain algorithm into the graph. The efficiency of the proposed algorithms is widely investigated in this article. By doing so, several evaluations have been performed over three standard real-world data sets, namely Enron Email, Cit-HepTh, and Facebook data sets. The obtained results indicate the superiority of the proposed algorithm with respect to the execution time as an efficiency metric. Likewise, the results show the modularity of the proposed algorithm as another effectiveness metric compared with the other well-known related algorithms.

Emerging multitenant cloud computing ecosystems allow multiple applications to share virtualized pool of computing and networking resources. As a result, such ecosystems are becoming increasingly prone to data privacy concerns (personal data leakages and unauthorized access). While cloud computing providers support robust security and privacy mechanisms (e.g., public key cryptography, firewalls, and virtual private networks, among many others), they lack mechanisms and frameworks to monitor, audit, and verify these data privacy concerns. The emergence of data protection regulations around the world, such as General Data Protection Regulation in Europe and the Data Protection Act in the U.K., further emphasizes the need to overcome these privacy limitations. In this article, a novel technique for monitoring, auditing, and verifying the operations carried out on a user’s personal data in cloud computing ecosystems is proposed. Our research methodology leverages distributed ledger technologies (e.g., blockchain and smart contracts) for developing an immutable recording technique, which transparently logs, monitors, and verifies the operations carried out on user data. Using a healthcare pharmacy scenario and extensive real-world experiments, we validate the feasibility of the proposed technique. The proposed work handles a large pool of requests ( $>$ 13K) ensuring minimal latency ( $\approx$ 50–60 ms) and overheads for three different service packages varied with respect to the number of actors and operations.

The European General Data Protection Regulation (GDPR) has had a far-reaching impact on data privacy for cloud providers. GDPR influences access to, storage, and transmission of personal data, requiring these operations to be verified by cloud users through explicit consent prior to execution. GDPR rules implemented for such operations can be ambiguous and often open to interpretation, making manual verification a time consuming and error prone process for cloud providers. An encoding of GDPR rules is described, with each operation carried out using these rules recorded into a Blockchain for auditing purposes. This work shows how some GDPR rules can appear as opcodes in smart contracts to verify the operations of providers on user data in an automatic way. An abstract model is designed to demonstrate how cloud providers can access and deploy such contracts through a Blockchain-based virtual machine. A case study is used to demonstrate how this approach can be used in practice. The case study uses several design patterns and smart contracts to verify provider operations, including read, write, execution and transfer on user data. Validation is undertaken by deploying the smart contracts in a Blockchain test network to investigate the execution costs of GDPR compliance checking.

The recognition of capabilities supplied by cloud systems is presently growing. Collecting or sharing healthcare data and sensitive information especially during the Covid-19 pandemic has motivated organizations and enterprises to leverage the upsides coming from cloud-based applications. However, the privacy of electronic data in such applications remains a significant challenge for cloud vendors to adapt their solutions with existing privacy legislation standards such as general data protection regulation (GDPR). This article first proposes a formal model and verification for data usage requests of providers in a cloud composite service using a model checking tool. A cloud pharmacy scenario is presented to illustrate the connectivity of providers in the composite service and the stream of their requests for both collection and movement of patient data. A set of verifications is then undertaken over the pharmacy service in accordance with three significant GDPR obligations, namely user consent, data access, and data transfer. Following that, the article designs and implements a cloud container virtualization based on the verified formal model realizing GDPR requirements. The container makes use of some enforcement smart contracts to only proceed with the providers’ requests that are compliant with GDPR. Finally, several experiments are provided to investigate the performance of our approach in terms of time, memory, and cost.