Wireless network security and cracking security key

Wireless technology gives us mobility easy access to the computer network without copper wires. With the increased use of wireless technology, more and more Wi-Fi hotspots, rising number of cell phones, PDAs, Tablet PC, laptops (devices with Wi-Fi module), wireless security is an ever increasing issue for many organizations. In other words wireless networks add another entry point into a network for hackers. Because this technology is relatively new there is many security vulnerabilities. In this paper, we study security on the wireless network and its vulnerability, also we give examples of how hackers can crack security key, explode vulnerabilities and attack the wireless network. We consider the weak points of these networks in order to suggest ways and methods to ensure a good protection

Gestion sécurisée de données personnelles

National audienceDans cet article, nous présentons certains travaux de l’équipe SMIS les plus enlien avec le nouveau modèle du Web Personnel Sécurisé. Nous introduisons d’aborden section 2 une famille d’architectures radicalement différente de celle du Web actuel où l’individu exerce un contrôle sur ses données personnelles depuis des composants personnels sécurisés situés aux extrémités du réseau, avec de fortes garantiesde non contournement de ses directives. Puis, nous présentons en section 3 deuxtypes de problèmes de recherche sous-jacents à ces architectures : d’une part, la problématique de gestion de données embarquées sur du matériel sécurisé, et d’autrepart, la problématique de calcul distribué sécurisé mettant en jeu de grandes populations de serveurs personnels. Nous illustrons cette approche en section 4 avec unexemple d’expérimentation terrain, le « Dossier Médico-Social Partagé » (DMSP),qui exploite ces concepts. Enfin, nous concluons en section 5 avec une discussionsur l’adoptabilité de cette approch

Privacy-Preserving Record Linkage for Big Data: Current Approaches and Research Challenges

The growth of Big Data, especially personal data dispersed in multiple data sources, presents enormous opportunities and insights for businesses to explore and leverage the value of linked and integrated data. However, privacy concerns impede sharing or exchanging data for linkage across different organizations. Privacy-preserving record linkage (PPRL) aims to address this problem by identifying and linking records that correspond to the same real-world entity across several data sources held by different parties without revealing any sensitive information about these entities. PPRL is increasingly being required in many real-world application areas. Examples range from public health surveillance to crime and fraud detection, and national security. PPRL for Big Data poses several challenges, with the three major ones being (1) scalability to multiple large databases, due to their massive volume and the flow of data within Big Data applications, (2) achieving high quality results of the linkage in the presence of variety and veracity of Big Data, and (3) preserving privacy and confidentiality of the entities represented in Big Data collections. In this chapter, we describe the challenges of PPRL in the context of Big Data, survey existing techniques for PPRL, and provide directions for future research.This work was partially funded by the Australian Research Council under Discovery Project DP130101801, the German Academic Exchange Service (DAAD) and Universities Australia (UA) under the Joint Research Co-operation Scheme, and also funded by the German Federal Ministry of Education and Research within the project Competence Center for Scalable Data Services and Solutions (ScaDS) Dresden/Leipzig (BMBF 01IS14014B)

Exécutions de requêtes respectueuses de la vie privée par utilisation de composants matériels sécurisés

Current applications, from complex sensor systems (e.g. quantified self) to online e-markets acquire vast quantities of personal information which usually end-up on central servers. This massive amount of personal data, the new oil, represents an unprecedented potential for applications and business. However, centralizing and processing all one's data in a single server, where they are exposed to prying eyes, poses a major problem with regards to privacy concern.Conversely, decentralized architectures helping individuals keep full control of their data, but they complexify global treatments and queries, impeding the development of innovative services.In this thesis, we aim at reconciling individual's privacy on one side and global benefits for the community and business perspectives on the other side. It promotes the idea of pushing the security to secure hardware devices controlling the data at the place of their acquisition. Thanks to these tangible physical elements of trust, secure distributed querying protocols can reestablish the capacity to perform global computations, such as SQL aggregates, without revealing any sensitive information to central servers.This thesis studies the subset of SQL queries without external joins and shows how to secure their execution in the presence of honest-but-curious attackers. It also discusses how the resulting querying protocols can be integrated in a concrete decentralized architecture. Cost models and experiments on SQL/AA, our distributed prototype running on real tamper-resistant hardware, demonstrate that this approach can scale to nationwide applications.Les applications actuelles, des systèmes de capteurs complexes (par exemple auto quantifiée) aux applications de e-commerce, acquièrent de grandes quantités d'informations personnelles qui sont habituellement stockées sur des serveurs centraux. Cette quantité massive de données personnelles, considéré comme le nouveau pétrole, représente un important potentiel pour les applications et les entreprises. Cependant, la centralisation et le traitement de toutes les données sur un serveur unique, où elles sont exposées aux indiscrétions de son gestionnaire, posent un problème majeur en ce qui concerne la vie privée.Inversement, les architectures décentralisées aident les individus à conserver le plein de contrôle sur leurs données, toutefois leurs traitements en particulier le calcul de requêtes globales deviennent complexes.Dans cette thèse, nous visons à concilier la vie privée de l'individu et l'exploitation de ces données, qui présentent des avantages manifestes pour la communauté (comme des études statistiques) ou encore des perspectives d'affaires. Nous promouvons l'idée de sécuriser l'acquisition des données par l'utilisation de matériel sécurisé. Grâce à ces éléments matériels tangibles de confiance, sécuriser des protocoles d'interrogation distribués permet d'effectuer des calculs globaux, tels que les agrégats SQL, sans révéler d'informations sensibles à des serveurs centraux.Cette thèse étudie le sous-groupe de requêtes SQL sans jointures et montre comment sécuriser leur exécution en présence d'attaquants honnêtes-mais-curieux. Cette thèse explique également comment les protocoles d'interrogation qui en résultent peuvent être intégrés concrètement dans une architecture décentralisée. Nous démontrons que notre approche est viable et peut passer à l'échelle d'applications de la taille d'un pays par un modèle de coût et des expériences réelles sur notre prototype, SQL/AA

Scalable and approximate privacy-preserving record linkage

Record linkage, the task of linking multiple databases with the aim to identify records that refer to the same entity, is occurring increasingly in many application areas. Generally, unique entity identifiers are not available in all the databases to be linked. Therefore, record linkage requires the use of personal identifying attributes, such as names and addresses, to identify matching records that need to be reconciled to the same entity. Often, it is not permissible to exchange personal identifying data across different organizations due to privacy and confidentiality concerns or regulations. This has led to the novel research area of privacy-preserving record linkage (PPRL). PPRL addresses the problem of how to link different databases to identify records that correspond to the same real-world entities, without revealing the identities of these entities or any private or confidential information to any party involved in the process, or to any external party, such as a researcher. The three key challenges that a PPRL solution in a real-world context needs to address are (1) scalability to largedatabases by efficiently conducting linkage; (2) achieving high quality of linkage through the use of approximate (string) matching and effective classification of the compared record pairs into matches (i.e. pairs of records that refer to the same entity) and non-matches (i.e. pairs of records that refer to different entities); and (3) provision of sufficient privacy guarantees such that the interested parties only learn the actual values of certain attributes of the records that were classified as matches, and the process is secure with regard to any internal or external adversary. In this thesis, we present extensive research in PPRL, where we have addressed several gaps and problems identified in existing PPRL approaches. First, we begin the thesis with a review of the literature and we propose a taxonomy of PPRL to characterize existing techniques. This allows us to identify gaps and research directions. In the remainder of the thesis, we address several of the identified shortcomings. One main shortcoming we address is a framework for empirical and comparative evaluation of different PPRL solutions, which has not been studied in the literature so far. Second, we propose several novel algorithms for scalable and approximate PPRL by addressing the three main challenges of PPRL. We propose efficient private blocking techniques, for both three-party and two-party scenarios, based on sorted neighborhood clustering to address the scalability challenge. Following, we propose two efficient two-party techniques for private matching and classification to address the linkage quality challenge in terms of approximate matching and effective classification. Privacy is addressed in these approaches using efficient data perturbation techniques including k-anonymous mapping, reference values, and Bloom filters. Finally, the thesis reports on an extensive comparative evaluation of our proposed solutions with several other state-of-the-art techniques on real-world datasets, which shows that our solutions outperform others in terms of all three key challenges

A Scalable Blocking Framework for Multidatabase Privacy-preserving Record Linkage

Today many application domains, such as national statistics, healthcare, business analytic, fraud detection, and national security, require data to be integrated from multiple databases. Record linkage (RL) is a process used in data integration which links multiple databases to identify matching records that belong to the same entity. RL enriches the usefulness of data by removing duplicates, errors, and inconsistencies which improves the effectiveness of decision making in data analytic applications. Often, organisations are not willing or authorised to share the sensitive information in their databases with any other party due to privacy and confidentiality regulations. The linkage of databases of different organisations is an emerging research area known as privacy-preserving record linkage (PPRL). PPRL facilitates the linkage of databases by ensuring the privacy of the entities in these databases. In multidatabase (MD) context, PPRL is significantly challenged by the intrinsic exponential growth in the number of potential record pair comparisons. Such linkage often requires significant time and computational resources to produce the resulting matching sets of records. Due to increased risk of collusion, preserving the privacy of the data is more problematic with an increase of number of parties involved in the linkage process. Blocking is commonly used to scale the linkage of large databases. The aim of blocking is to remove those record pairs that correspond to non-matches (refer to different entities). Many techniques have been proposed for RL and PPRL for blocking two databases. However, many of these techniques are not suitable for blocking multiple databases. This creates a need to develop blocking technique for the multidatabase linkage context as real-world applications increasingly require more than two databases. This thesis is the first to conduct extensive research on blocking for multidatabase privacy-preserved record linkage (MD-PPRL). We consider several research problems in blocking of MD-PPRL. First, we start with a broad background literature on PPRL. This allow us to identify the main research gaps that need to be investigated in MD-PPRL. Second, we introduce a blocking framework for MD-PPRL which provides more flexibility and control to database owners in the block generation process. Third, we propose different techniques that are used in our framework for (1) blocking of multiple databases, (2) identifying blocks that need to be compared across subgroups of these databases, and (3) filtering redundant record pair comparisons by the efficient scheduling of block comparisons to improve the scalability of MD-PPRL. Each of these techniques covers an important aspect of blocking in real-world MD-PPRL applications. Finally, this thesis reports on an extensive evaluation of the combined application of these methods with real datasets, which illustrates that they outperform existing approaches in term of scalability, accuracy, and privacy