(Belga) Quatre membres de la milice d'extrême droite "Oath Keepers" ont été reconnus coupables lundi de sédition pour leur rôle dans l'assaut du Capitole, à l'issue du second procès organisé sur ce chef d'accusation extrêmement rare.

Depuis l'attaque du 6 janvier 2021, plus de 950 partisans de l'ex-président républicain Donald Trump ont été arrêtés et inculpés pour avoir semé le chaos dans le siège de la démocratie américaine. Parmi eux, seuls 14 militants de groupuscules d'extrême droite - neuf membres des "Oath Keepers" et cinq "Proud Boys" - ont été accusés de "sédition", un chef passible de 20 ans de prison qui implique d'avoir planifié l'usage de la force pour s'opposer au gouvernement. Faute de place suffisante dans le tribunal fédéral de Washington, la justice a organisé le procès des Oath Keepers, accusés de s'être entraînés et armés pour l'occasion, en deux temps. Un premier procès s'est conclu fin novembre par un verdict mitigé: le fondateur de cette milice, Stewart Rhodes, et un responsable local ont été déclarés coupables de sédition, mais leurs trois co-accusés ont été acquittés sur ce chef. Lundi, à l'issue du second procès, les jurés ont jugé coupables les quatre derniers Oath Keepers, des hommes âgés de 38 à 64 ans décrits comme de dangereux "traîtres" par l'accusation, mais comme des "fanfarons" par leurs avocats. Le procès des Proud Boys, dont leur leader Enrique Tarrio, s'est ouvert en décembre et était toujours en cours lundi, dans le même tribunal. (Belga)

Transactions on Data Privacy, Volume 12 Issue 2 (2019) has been published.

Transactions on Data Privacy, Volume 12 Issue 3 (2019) has been published.

Transactions on Data Privacy, Volume 13 Issue 1 (2020) has been published.

Transactions on Data Privacy, Volume 13 Issue 2 (2020) has been published.

Transactions on Data Privacy, Volume 13 Issue 3 (2020) has been published.

Transactions on Data Privacy, Volume 14 Issue 1 (2021) has been published.

Transactions on Data Privacy, Volume 14 Issue 2 (2021) has been published.

Transactions on Data Privacy, Volume 14 Issue 3 (2021) has been published.

Transactions on Data Privacy, Volume 15 Issue 1 (2022) has been published.

Transactions on Data Privacy, Volume 15 Issue 2 (2022) has been published.

Transactions on Data Privacy, Volume 15 Issue 3 (2022) has been published.

Transactions on Data Privacy, Volume 16 Issue 1 (2023) has been published.

Transactions on Data Privacy, Volume 16 Issue 3 (2023) has been published.

Transactions on Data Privacy, Volume 17 Issue 1 (2024) has been published.

Transactions on Data Privacy, Volume 17 Issue 2 (2024) has been published.

Transactions on Data Privacy, Volume 17 Issue 3 (2024) has been published.

by Mitchell Longan[1] Introduction The European Parliament is currently considering a proposal to address problems of piracy and other forms of copyright infringement associated with the digital world.[2] Article 13 of the proposed Directive on Copyright in the Digital Single

by Dr Kim Barker (University of Stirling) & Dr Olga Jurasz (Open University) The UK Government has recently published an Online Harms White Paper: initial consultation response. It is the cornerstone of the Government’s ongoing reform package which aims to

By Hasan Basri Cifci[1] In the world of 2014, the Commission of Facebook/WhatsApp merger case[2] concluded that integration and interoperation of Facebook and WhatsApp were unfeasible. However, Facebook integrated its three subsidiaries (WhatsApp, Instagram, and Facebook) under its brand in

C'est un classique. Juste aprï¿œs leur victoire, certains ï¿œlus locaux font voter une augmentation de leur salaire. Une dï¿œcision qui intervient au dï¿œbut de leur mandat, histoire que les ï¿œlecteurs aient le temps...

C'est la rentrￜe politique. Et qui dit rentrￜe, dit universitￜ d'ￜtￜ. Cette annￜe, la Rￜpublique en marche a vu les choses en grand en organisant ￜ la fois des journￜes...

Hasard du calendrier ou volontￜ de prￜserver le nouveau Premier ministre ? Selon Mediapart, une enquￜte judiciaire ouverte par le parquet de Perpignan, potentiellement gￜnante pour Jean Castex, a ￜtￜ...

Des files d'attente, la nuit, devant les prￜfectures, pour tenter d'obtenir un rendez-vous afin de demander ou de renouveler un titre de sￜjour. C'ￜtait la rￜalitￜ au dￜbut des annￜes...

L'information est passï¿œe inaperï¿œue mais elle ne manque pas de sel. Alors que la gestion de la crise du covid-19 par le gouvernement est trï¿œs contestï¿œe (au retard dans la livraison de masques s'ajoute...

A chaque jour, une nouvelle annonce. Cet ￜtￜ, le ministre de l'Intￜrieur, Gￜrald Darmanin, a multipliￜ les dￜplacements sur le terrain et les annonces. Pour lutter contre les pyromanes ￜ...

Through the unparalleled espousal of theory of planned behaviour, this study intends to significantly add to the current knowledge on social networking sites (SNS) in <i>eWOM</i> information and its role in defining intentions to buy green products. In specie, this study seeks to first investigate the part played by <i>attitude towards SNS information</i> in influencing the <i>acceptance of SNS information</i> and then by <i>acceptance of SNS information</i> in effecting the <i>green purchase intention</i>. Besides this, it also aims to analyse the influence exerted by first <i>credibility of SNS information</i> on <i>acceptance of SNS information</i> and then by <i>acceptance of SNS information</i> on <i>green purchase intention</i>. In doing so, it also examines how well the age of the SNS users moderates all these four associations.

Aiming at the problems of high error rate, low evaluation accuracy and low investment return in traditional methods, a random forest-based e-bank investment risk evaluation method is proposed. First, establish a scientific e-bank investment risk evaluation index system. Then, G1-COWA combined weighting method is used to calculate the weights of each index. Finally, the e-bank investment risk evaluation index data is taken as the input vector, and the e-bank investment risk evaluation result is taken as the output vector. The random forest model is established and the result of e-banking investment risk evaluation is obtained. The experimental results show that the maximum relative error rate of this method is 4.32%, the evaluation accuracy range is 94.5~98.1%, and the maximum return rate of e-banking investment is 8.32%. It shows that this method can accurately evaluate the investment risk of electronic banking.

The operation of supply chain finance faces various risks, therefore, studying the operational risks of supply chain finance and corresponding preventive measures is of great significance. Firstly, classify the types of operational risks in supply chain finance. Secondly, based on the risk classification results, the decision tree method is used to evaluate the operational risks of supply chain finance. Finally, based on the risk assessment results, targeted risk prevention measures for supply chain finance operations are proposed, such as strengthening supplier management, optimising logistics and warehouse management, risk analysis and monitoring, and strengthening information security and data protection. The case analysis results show that the accuracy of the evaluation results of this method is higher, and the risk coefficient has been significantly reduced after applying this method, indicating that it can effectively reduce supply chain risk.

In order to solve the problems of long prediction consumption time and many prediction iterations existing in traditional prediction models, an e-commerce growth prediction model based on grey Markov chain is proposed. The Scrapy crawler framework is used to collect a variety of e-commerce data from e-commerce websites, and the feedforward neural network model is used to clean the collected data. With the cleaned e-commerce data as the input vector and the e-commerce growth prediction results as the output vector, an e-commerce growth prediction model based on the grey Markov chain is built. The prediction model is improved by using the background value optimisation method. After training the model through the improved particle swarm optimisation algorithm, accurate e-commerce growth prediction results are obtained. The experimental results show that the maximum time consumption of e-commerce growth prediction of this model is only 0.032, and the number of iterations is small.

To achieve the benefits as much as possible, it is required to identify the available PEV capacity and prepare scheduling plans based on that. The analysis revealed that the risk-based scheduling of the microgrid could reduce the financial risk completely from $9.89 to $0.00 and increases the expected operation cost by 24% from $91.38 to $112.94, in turn. This implies that the risk-averse decision-maker tends to spend more money to reduce the expected risk-in-cost by using the proposed downside risk management technique. At the end, by the help of fuzzy satisfying method, the suitable risk-averse strategy is determined for the studied case.

The problem of dispatching is key to management of shared bicycles. Considering the number of borrowing and returning events during the dispatching period, optimisation plans of shared bicycles dispatching are studied in this paper. Firstly, the dispatching model of shared bicycles is built, which regards the dispatching cost and lost demand as optimised objectives. Secondly, the solution algorithm is designed based on non-dominated Genetic Algorithm. Finally, a case is given to illustrate the application of the method. The research results show that the method proposed in the paper can get optimised dispatching plans, and the model considering borrowing and returning during dispatching period has better effects with a 39.3% decrease in lost demand.

Several challenges exist in disseminating multi-level secure (MLS) data in multi-domain environments. First, the security domains participating in data dissemination generally use different MLS labels and lattice structures. Second, when MLS data objects are transferred across multiple domains, there is a need for an agreed security policy that must be properly applied, and correctly enforced for the data objects. Moreover, the data sender may not be able to predetermine the data recipients located beyond its trust boundary. To address these challenges, we propose a new framework that enables secure dissemination and access of the data as intended by the owner. Our novel framework leverages simple public key infrastructure and active bundle, and allows domains to securely disseminate data without the need to repackage it for each domain.

Image forgery detection techniques refer to the process of detecting manipulated or altered images, which can be used for various purposes, including malicious intent or misinformation. Image forgery detection is a crucial task in digital image forensics, where researchers have developed various techniques to detect image forgery. These techniques can be broadly categorised into active, passive, machine learning-based and hybrid. Active approaches involve embedding digital watermarks or signatures into the image during the creation process, which can later be used to detect any tampering. On the other hand, passive approaches rely on analysing the statistical properties of the image to detect any inconsistencies or irregularities that may indicate forgery. In this paper for the detection of scaling and cropping attack a deep learning method has been proposed using ResNet. The proposed method (Res-Net-Adam-Adam) is able to achieve highest amount of accuracy of 99.14% (0.9914) while detecting fake and real images.

The confluence of the internet of things (IoT) with smart grids has ushered in a paradigm shift in energy management, promising unparalleled efficiency, economic robustness and unwavering reliability. However, this integrative evolution has concurrently amplified the grid's susceptibility to cyber intrusions, casting shadows on its foundational security and structural integrity. Machine learning (ML) and deep learning (DL) emerge as beacons in this landscape, offering robust methodologies to navigate the intricate cybersecurity labyrinth of IoT-infused smart grids. While ML excels at sifting through voluminous data to identify and classify looming threats, DL delves deeper, crafting sophisticated models equipped to counteract avant-garde cyber offensives. Both of these techniques are united in their objective of leveraging intricate data patterns to provide real-time, actionable security intelligence. Yet, despite the revolutionary potential of ML and DL, the battle against the ceaselessly morphing cyber threat landscape is relentless. The pursuit of an impervious smart grid continues to be a collective odyssey. In this review, we embark on a scholarly exploration of ML and DL's indispensable contributions to enhancing cybersecurity in IoT-centric smart grids. We meticulously dissect predominant cyber threats, critically assess extant security paradigms, and spotlight research frontiers yearning for deeper inquiry and innovation.

File transfer is always handled by a separate service, sometimes it is a third-party service in videoconferencing. When sending files during a video session, file data flow and video stream are independent of each other. Encryption is a mature method to ensure file security. However, it still has the chance to leave footprints on the intermediate forwarding machines. These footprints can indicate that a file once passed through, some protocol-related logs give clues to the hackers' later investigation. This work proposes a file-sending scheme through the video stream using blockchain and steganography. Blockchain is used as a file slicing and linkage mechanism. Steganography is applied to embed file pieces into video frames that are continuously generated during the session. The scheme merges files into the video stream with no file transfer protocol use and no extra bandwidth consumed by the file to provide trackless file transmission during the video communication.

At present, there is a need for an authentic and fast approach to certificate verification. Which verifies and authenticates the certificates to reduce the extent of duplicity and time. An academic certificate is significant for students, the government, universities, and employers. Academic credentials play a vital role in the career of students. A few people manipulate academic documents for their benefit. There are cases identified where people produced fake academic certificates for jobs or higher education admission. Various research works are developing a secure model to verify genuine academic credentials. This research article proposed a new security model which contains several security algorithms such as timestamps, hash function, digital signature, steganography, and blockchain. The proposed model issues secure digital academic certificates. It enhanced security measures and automated educational certificate verification using blockchain technology. The advantages of the proposed model are automated, cost-effective, secured, traceable, accurate, and time-saving.

Image encryption is a critical and attractive issue in digital image processing that has gained approval and interest of many researchers in the world. A proposed hybrid encryption method was implemented by using the combination of the Nahrain chaotic map with a well-known optimised algorithm namely the grey wolf optimisation (GWO). It was noted from analysing the results of the experiments conducted on the new hybrid algorithm, that it gave strong resistance against expected statistical invasion as well as brute force. Several statistical analyses were carried out and showed that the average entropy of the encrypted images is near to its ideal information entropy.

The current study has outlined slow fashion (SF) research trends and created a future research agenda for this field. It is a thorough analysis of the literature on slow fashion. Numerous bibliometric features of slow fashion have been discussed in the paper. This study comprises 182 research articles from the Scopus database. The database was utilised for bibliometric analysis. To identify certain trends in the area of slow fashion, a bibliometric study is done. For bibliometric analysis, the study employed R-software (the Biblioshiny package). Here, VOSviewer software is used to determine the co-occurrence of authors, countries, sources, etc. The study has outlined the gap that still exists in the field of slow fashion. Here, the research outcome strengthens the domain of slow fashion for sustainable consumption. The study findings will be useful for policymakers, industry professionals, and researchers.

Access to quality healthcare is a fundamental right but unfortunately, India suffers from gender disparities in healthcare access. Telemedicine has the potential to improve access to healthcare services for women by eliminating traditional barriers. Therefore, our research aims to investigate the factors influencing the adoption of telemedicine among Indian women. This study has collected 442 responses and analysed them through structural equation modelling. The result indicates a strong and positive connection between the willingness to adopt telemedicine services and factors like performance expectancy, perceived benefits, e-health literacy, and perceived reliability. Notably, perceived reliability emerges as the most impactful predictor, closely followed by perceived benefits, while factors like effort expectancy and user resistance show no significant influence. This underscores the pivotal role of reliability and perceived benefits in shaping women's inclination toward adopting telemedicine. The study provides practical insights for telemedicine providers and policymakers to customise strategies and policies for effective promotion.

This systematic literature study employed bibliometric analysis to identify workplace mindfulness-related methods and practices in literature published from 2000 to 2020 by leading nations, institutions, journals, authors, and keywords. We also assessed the impact of workplace mindfulness research papers. Scopus analysis tools provided a literature report for 638 Scopus articles used in the study. Using VOSviewer, leading nations, institutions, articles, authors, journals, and keyword co-occurrence network maps were constructed. PRISMA was used to identify 56 publications to recognise workplace mindfulness literature's significant achievements. The research's main contribution is a deep review of neurological mindfulness and psychological measuring tools as workplace mindfulness tool categories. The study is the first to use the PRISMA technique to capture the essential contributions of workplace mindfulness papers from 2000 to 2020.

Although artificial intelligence provides a new method to gather, process, analyse data, generate insights, and offer customised solutions, such methods could change how marketers deal with customers, and there is a lack of literature to portray the application of artificial intelligence in marketing. This study aims to recognise and portray the use of artificial intelligence from a marketing standpoint, as well as to provide a conceptual framework for the application of artificial intelligence in marketing. This study uses a systematic literature review analysis as a research method to achieve the aims. Data from 142 articles were extracted from the Scopus database using relevant search terms for artificial intelligence and marketing. The systematic review identified significant usage of artificial intelligence in conversational artificial intelligence, content creation, audience segmentation, predictive analytics, personalisation, paid ads, sales forecasting, dynamic pricing, and recommendation engines and the bibliometric analysis produced the trend in co-authorship, citation, bibliographic coupling, and co-citation analysis. Practitioners and academics may use this study to decide on the marketing area in which artificial intelligence can be invested and used.

Supply chain management practices are vital for success and survival in today's competitive Indian retail market. The advent of COVID-19 pandemic necessitates a digital disruption in retail supply chain management centred on efficient technology like blockchain in order to enhance supply chain performance. The present research aims to decipher the nature of associations between supply chain management practices, blockchain technology adoption and supply chain performance in retail firms. The research is based on primary survey of specific food and grocery retailers operating on a supermarket format stores in two Indian cities. The findings pointed towards the presence of significant and positive association of all the constructs with each other. Moreover, the mediating role of blockchain technology adoption was also revealed, i.e., it partially mediates the effects of supply chain management practices on supply chain performance.