International audience

In this paper, we present MobiRDF , a novel cloud-based approach designed for the efficient and scalable management of RDF data, enabling real-time sharing and editing. MobiRDF offers two main services: (i) Partial Replication of RDF Graphs : This service facilitates the selective replication of RDF graphs on mobile devices, addressing their inherent resource limitations. Our partial graph selector allows using only the useful data requested by the user from the RDF graph instead of storing the entire RDF graph, which enables efficient data storage and retrieval. (ii) Collaboration Protocol : This protocol provides synchronization mechanisms for collaborative work in a fully decentralized manner. It uses commutativity-based consistency model to maintain the consistency of the shared RDF graph, ensuring seamless collaboration among users. The heavier computational tasks, such as dynamic group management, synchronization merging, and reasoning processes, are managed in the Cloud, optimizing the performance of resource-constrained mobile devices. The key novelty of MobiRDF is its ability to ensure both syntactic and semantic consistency of shared RDF data, through reasoning processes using the Closed-World Assumption (CWA) for inferring new triples. Experimental evaluations show that MobiRDF is efficient in terms of network bandwidth and energy consumption, validating its effectiveness in real-world scenarios.

Despite the enormous data processing capacity available in big data frameworks, obtaining appropriate and private responses to large-scale queries without revealing sensitive information is still a challenging problem. In this paper, we address the problem of combining offline sampling techniques for space efficiency in multidimensional databases and Differential Privacy (DP) to protect sensitive data. We present our framework SLIM-View, which uses a novel sampling technique relying on a bi-objective optimization to decide the best sample size and the exponential mechanism to select the best sample while ensuring privacy. Our extensive experiments demonstrate that SLIM-View outperforms existing approaches by orders of magnitude in terms of utility and scalability while ensuring the same level of privacy.

Social Coding Platforms (SCPs) like GitHub have become central to modern software engineering thanks to their collaborative and version-control features. Like in mainstream Online Social Networks (OSNs) such as Facebook, users of SCPs are subjected to privacy attacks and threats given the high amounts of personal and project-related data available in their profiles and software repositories. However, unlike in OSNs, the privacy concerns and practices of SCP users have not been extensively explored nor documented in the current literature. In this work, we present the preliminary results of an online survey (N=105) addressing developers’ concerns and perceptions about privacy threats steaming from SCPs. Our results suggest that, although users express concern about social and organisational privacy threats, they often feel safe sharing personal and project-related information on these platforms. Moreover, attacks targeting the inference of sensitive attributes are considered more likely than those seeking to re-identify source-code contributors. Based on these findings, we propose a set of recommendations for future investigations addressing privacy and identity management in SCPs.

Managing and storing big data is non-trivial for traditional relational databases (RDBMS). Therefore, the NoSQL (Not Only SQL) database management system emerged. It is ca-pable of handling the vast amount and the heterogeneity of data. In this research, we are interested in one of its trending types, the graph database, namely, the Directed Property Graph (DPG). This type of database is powerful in dealing with complex relationships ($\mathrm{e}.\mathrm{g}$., social networks). However, its sen-sitive and private data must be protected against unauthorized access. This research proposes a security model that aims at exploiting and combining the benefits of Access Control, View-Based, and Query-Rewriting approaches. This is a novel combination for securing DPG.

As fraud and cybercrime become more frequent and sophisticated, preventing users from being exposed to risk is a significant challenge for the scientific community. Current banking systems use One Time Password (OTP) sent to the mobile phone to prevent user impersonation attacks. However, it could be compromised by man-in-the-middle attacks. It also presents usability and unavailability problems if the SMS service is inaccessible, especially when traveling or in case of a damaged or stolen phone. Using Machine Learning (ML) techniques to detect and prevent fraudulent actions allows financial organizations to stay one step ahead of fraudsters, whose scale and sophistication continue to grow. Specifically, ML systems can be used to identify additional authentication factors to make the system more robust against attacks. To prevent identity theft attacks and minimize fraudulent transactions, we propose in this work a comprehensive design that combines adaptive multi-factor authentication and adaptive fraud detection for banking systems based on the use of ML technology.

Nowadays, information retrieval systems face significant challenges in providing users with accurate information due to the enormous growth of information. To better reformulate the query and narrow its results, semantic query expansion techniques add semantically related terms to the original query. However, semantic query expansion for Arabic queries is still a challenge due to the lack of rich semantic sources. Most of the existing solutions rely on using either English sources or specific-domain Arabic ontologies. Using English sources requires a translation phase which may lead to query drift, thus providing unrelated expansion terms. In this paper, we provide an overview of the query expansion approaches. Besides, we propose a hybrid comprehensive reference framework for Arabic semantic query expansion that overcomes the lack of Arabic semantic sources by using rich English ontologies to complement the limited Arabic sources (Arabic Wordnet) currently available. It ensures the Arabic-English translation process using a customized machine learning translation model to avoid query drifting. It also transforms natural language to SPARQL (an ontology query language) to easily query English sources (e.g., DBpedia). For enhanced accuracy, it provides an optimization module where meta-heuristics can be used for pertinent terms selection. This work represents a step forward in combining English sources and AI to design a practical Arabic semantic expansion.

Socia1 media data has been mined over the years to predict individual sensitive attributes such as political and religious beliefs. Indeed, mining such data can improve the user experience with personalization and freemium services. Still, it can also be harmful and discriminative when used to make critical decisions, such as employment. In this work, we investigate social media privacy protection against attribute inference attacks using machine learning explainability and adversarial defense strategies. More precisely, we propose FOX (FOoling with eXplanations), an adversarial attack framework to explain and fool sensitive attribute inference models by generating effective adversarial reactions. We evaluate the performance of FOX with other SOTA baselines in a black-box setting by attacking five gender attribute classifiers trained on Facebook pictures reactions, specifically (i) comments generated by Facebook users excluding the picture owner, and (ii) textual tags (i.e., alttext) generated by Facebook. Our experiments show that FOX successfully fools (about 99.7% and 93.2% of the time) the classifiers, outperforms the SOTA baselines and gives a good transferability of adversarial features.

Collaborative editing applications for cloud environment play an important role in many fields and communities since they allow users to communicate and collaborate using their mobile devices. Mobile devices are usually cloned in the cloud to minimize computation cost and energy consumption. Moreover, these mobile and cloud interactions lead to online and offline switching in easy and continuous ways in order to edit shared multimedia documents. However, the main concern of these applications is still maintaining consistent and secure copies of the shared documents with low latency and high local responsiveness. Indeed, appropriate access control models are needed to preserve the features of collaborative editing applications when combining mobile and cloud environments. In this paper, we present a study on existing cloud-based collaborative editors and the current state of the art of access control models used in the collaborative edition context. This study has raised a series of shortcomings that have enabled us to sketch a new access model for deploying securely mobile collaborative editing applications in the cloud.

Collaborative editors are one of the most popular collaborative tools. They are being widely used thanks to the success of data sharing platforms where in most of the cases, data is shared with the intent to be edited simultaneously by many users who are distributed and dispersed across the globe. Keeping shared data synchronized is resource-intensive. With the emergence of collaborative editors over mobile devices, the challenges to meet increasing communication and computation are more and more noticeable. Indeed, the centralized cloud-based architecture incurs high delays which prevents users from seeing shared data updates in real-time fashion. In this paper, we give an overview on existing cloud-based works and propose a new edge-based architecture for collaborative editors. Mobiles that are managed by the same edge are cloned to offload resource-intensive tasks to the edge node, whereas only lightweight edition components are handled locally. This provides a more effective solution to manage concurrency and collaboration on mobile devices.

Attributes such as interests, workplace and relationship status in an Online Social Network (OSN) profile introduce a user to other OSN users. They can contribute to building new friendships as well as reviving and enhancing existing ones. However, the personal data revealed by the user himself or by his vicinity, i.e., his OSN friends, can also make him vulnerable to many privacy harms such as identity theft, stalking or sexual predation. So users have to carefully select the privacy settings for their profile attributes by keeping in mind the trade-off between privacy and social benefit. In this paper, we propose a usercentric two-phase approach, based on Integer Programming, to choose the right privacy settings. Our model assists the user to understand which privacy harms he can avoid, after tolerating residual risks, given his desired social benefit requirements and suggests the privacy settings he should adopt to achieve the maximum social benefit. Thus, users' choices are based on both privacy risks and benefits, a view supported by the EU General Data Protection Regulation (GDPR). We have tested our approach on user profiles with varying vicinities and social benefit requirements.

The problem of private publication of graph data has attracted a lot of attention recently. The prevalence of differential privacy makes the problem more promising. However, a large body of existing works on differentially private release of graphs have not answered the question about the upper bounds of privacy budgets. In this paper, for the first time, such a bound is provided. We prove that with a privacy budget of O(log n), there exists an algorithm capable of releasing a noisy output graph with edge edit distance of O(1) against the true graph. At the same time, the complexity of our algorithm Top-m Filter is linear in the number of edges m. This lifts the limits of the state-of-the-art, which incur a complexity of O(n2) where n is the number of nodes and runnable only on graphs having n of tens of thousands.

Rather than anonymizing social graphs by generalizing them to super nodes/edges or adding/removing nodes and edges to satisfy given privacy parameters, recent methods exploit the semantics of uncertain graphs to achieve privacy protection of participating entities and their relationships. These techniques anonymize a deterministic graph by converting it into an uncertain form. In this paper, we propose a general obfuscation model based on uncertain adjacency matrices that keep expected node degrees equal to those in the unanonymized graph. We analyze two recently proposed schemes and their fitting into the model. We also point out disadvantages in each method and present several elegant techniques to fill the gap between them. Finally, to support fair comparisons, we develop a new tradeoff quantifying framework by leveraging the concept of incorrectness. Experiments on large social graphs demonstrate the effectiveness of our schemes.

We aim to designe decentralized collaborative editing system. Keeping in mind that such system becoming more complex and care taking, we take in account some important properties like out-of-order execution of concurrent operations and data convergence. In this paper, we introduce a novel linear data structure based on unique identifier scheme for indexed communication. These identifiers are still real numbers with a specific controlled pattern of precision. This noval scheme preserves data convergence, provides good performance and avoids overheads as compared to other available approaches. We test our approach by performing experiments and we implemented our idea successfully.

The operational transformation (OT) approach, used in many collaborative editors, allows a group of users to concurrently update replicas of a shared object and exchange their updates in any order. The basic idea is to transform any received update operation before its execution on a replica of the object. Concretely, OT consists of a centralized/decentralized integration procedure and a transformation function. In the context of decentralized integration, designing transformation functions for achieving convergence of object replicas is a critical and challenging issue. Indeed, the transformation functions proposed in the literature are all revealed inefficient. In this paper, we investigate the existence of transformation functions. From the theoretical point of view, two properties, named TP1 and TP2, are necessary and sufficient to ensure convergence. Using controller synthesis technique, we show that there are some transformation functions, which satisfy TP1 for the basic signatures of insert and delete operations. But, there is no transformation function, which satisfies both TP1 and TP2. Consequently, a transformation function which satisfies both TP1 and TP2 must necessarily have additional parameters in the signatures of some update operations. We propose, in this paper, a new transformation function and show formally that it ensures convergence.

It is increasingly common to find XML views used to enforce access control as found in many applications and commercial database systems. To overcome the overhead of view materialization and maintenance, XML views are necessarily virtual. With this comes the need for answering XML queries posed over virtual views, by rewriting them into equivalent queries on the underlying documents. A major concern here is that query rewriting for recursive XML views is still an open problem, and proposed approaches deal only with non-recursive XML views. Moreover, a small number of works have studied the access rights for updates. In this paper, we present SVMAX (Secure and Valid MAnipulation of XML), the first system that supports specification and enforcement of both read and update access policies over arbitrary XML views (recursive or non). SVMAX defines general and expressive models for controlling access to XML data using significant class of XPath queries and in the presence of the update primitives of W3C XQuery Update Facility. Furthermore, SVMAX features an additional module enabling efficient validation of XML documents after primitive updates of XQuery. The wide use of W3C standards makes of SVMAX a useful system that can be easily integrated within commercial database systems as we will show. We give extensive experimental results, based on real-life DTDs, that show the efficiency and scalability of our system.

This paper describes the design and implementation of a Decentralized Shared Calendar (abbreviated as DeSCal), a distributed application which provides users a decentralized infrastructure to share their calendar events with selected users in a dynamic group. Although being a distributed application, DeSCal is as responsive as a personal calendar. It achieves this high responsiveness by keeping a local copy of the shared calendar at each participating user. Consistency of these replicated copies of the shared calendar is carried out in a decentralized fashion using Operational Transformation (OT) approach. OT allows users to concurrently modify the shared calendar and exchange their updates in any order since it ensures the convergence of copies of the shared calendar in all cases. To prevent unauthorized access by illegal users, DeSCal is endowed with access control mechanism on the local copy of the shared calendar. It employs a flexible access control model based on replicating the access data-structure at each user site to overcome the latency problem. Also, access control on the shared calendar events is dynamic i.e. users are able to change the access rights on their shared calendar events at any point of time after the creation of an event. In short, DeSCal is totally decentralized, scalable and self-configurable i.e., no need of a third party to manage it.

The mobile phone technologies are becoming pervasive in recent years. These items such as IPhones, IPad and Androïds are very attractive since they provide relatively good resources for a mobile device. Several works aim at integrating desktop applications in these tools to make them closer

In collaborative editing, consistency maintenance of the copies of shared data is a critical issue. In the last decade, operational transformation (OT) approach revealed as a suitable mechanism for maintaining consistency. Unfortunately, none of the published propositions relying on this approach are able to satisfy the mandatory correctness properties TP1and TP2defined in the Ressel's framework. This paper addresses this correctness issue by proposing a new way to model shared state by retaining tombstones when elements are removed. An instantiation of the proposed model for a linear data structure and the related transformation functions are provided

Peer-to-peer (P2P) networks are very efficient for distributing content. We want to use this potential to allow not only distribution but collaborative editing of this content. Existing collaborative editing systems are centralised or depend on the number of sites. Such systems cannot scale when deployed on P2P networks. In this paper, we propose a new model for building a collaborative editing system. This model is fully decentralised and does not depend on the number of sites.

Reconciliating divergent data is an important issue in concurrent engineering, mobile computing and software configuration management. Currently, a lot of synchronizers or merge tools perform reconciliations. However, they do not define what is the correctness of their synchronisation. In this paper, we propose to use a transformational approach as the basic model for reasonning about synchronisation. We propose an algorithm and specific transformation functions that realize a file system synchronisation. Unlike classic synchronizers, our synchronizer ensures properties of convergence, causality and intention preservation and is extensible to new data types.