Conception de protocoles cryptographiques préservant la vie privée pour les services mobiles sans contact

The increasing number of worldwide mobile platforms and the emergence of new technologies such as the NFC (Near Field Communication) lead to a growing tendency to build a user's life depending on mobile phones. This context brings also new security and privacy challenges. In this thesis, we pay further attention to privacy issues in NFC services as well as the security of the mobile applications private data and credentials namely in Trusted Execution Environments (TEE). We first provide two solutions for public transport use case: an m-pass (transport subscription card) and a m-ticketing validation protocols. Our solutions ensure users' privacy while respecting functional requirements of transport operators. To this end, we propose new variants of group signatures and the first practical set-membership proof that do not require pairing computations at the prover's side. These novelties significantly reduce the execution time of such schemes when implemented in resource constrained environments. We implemented the m-pass and m-ticketing protocols in a standard SIM card: the validation phase occurs in less than 300ms whilst using strong security parameters. Our solutions also work even when the mobile is switched off or the battery is flat. When these applications are implemented in TEE, we introduce a new TEE migration protocol that ensures the privacy and integrity of the TEE credentials and user's private data. We construct our protocol based on a proxy re-encryption scheme and a new TEE model. Finally, we formally prove the security of our protocols using either game-based experiments in the random oracle model or automated model checker of security protocols.Avec l'émergence de nouvelles technologies telles que le NFC (Communication à champ proche) et l'accroissement du nombre de plates-formes mobiles, les téléphones mobiles vont devenir de plus en plus indispensables dans notre vie quotidienne. Ce contexte introduit de nouveaux défis en termes de sécurité et de respect de la vie privée. Dans cette thèse, nous nous focalisons sur les problématiques liées au respect de la vie privée dans les services NFC ainsi qu’à la protection des données privées et secrets des applications mobiles dans les environnements d'exécution de confiance (TEE). Nous fournissons deux solutions pour le transport public: une solution utilisant des cartes d'abonnement (m-pass) et une autre à base de tickets électroniques (m-ticketing). Nos solutions préservent la vie privée des utilisateurs tout en respectant les exigences fonctionnelles établies par les opérateurs de transport. À cette fin, nous proposons de nouvelles variantes de signatures de groupe ainsi que la première preuve pratique d’appartenance à un ensemble, à apport nul de connaissance, et qui ne nécessite pas de calculs de couplages du côté du prouveur. Ces améliorations permettent de réduire considérablement le temps d'exécution de ces schémas lorsqu’ils sont implémentés dans des environnements contraints par exemple sur carte à puce. Nous avons développé les protocoles de m-passe et de m-ticketing dans une carte SIM standard : la validation d'un ticket ou d'un m-pass s'effectue en moins de 300ms et ce tout en utilisant des tailles de clés adéquates. Nos solutions fonctionnent également lorsque le mobile est éteint ou lorsque sa batterie est déchargée. Si les applications s'exécutent dans un TEE, nous introduisons un nouveau protocole de migration de données privées, d'un TEE à un autre, qui assure la confidentialité et l'intégrité de ces données. Notre protocole est fondé sur l’utilisation d’un schéma de proxy de rechiffrement ainsi que sur un nouveau modèle d’architecture du TEE. Enfin, nous prouvons formellement la sécurité de nos protocoles soit dans le modèle calculatoire pour les protocoles de m-pass et de ticketing soit dans le modèle symbolique pour le protocole de migration de données entre TEE

Enhancing Confidentiality and Privacy Preservation in e-Health to Enhanced Security

Electronic health (e-health) system use is growing, which has improved healthcare services significantly but has created questions about the privacy and security of sensitive medical data. This research suggests a novel strategy to overcome these difficulties and strengthen the security of e-health systems while maintaining the privacy and confidentiality of patient data by utilising machine learning techniques. The security layers of e-health systems are strengthened by the comprehensive framework we propose in this paper, which incorporates cutting-edge machine learning algorithms. The suggested framework includes data encryption, access control, and anomaly detection as its three main elements. First, to prevent unauthorised access during transmission and storage, patient data is secured using cutting-edge encryption technologies. Second, to make sure that only authorised staff can access sensitive medical records, access control mechanisms are strengthened using machine learning models that examine user behaviour patterns. This research's inclusion of machine learning-based anomaly detection is its most inventive feature. The technology may identify variations from typical data access and usage patterns, thereby quickly spotting potential security breaches or unauthorised activity, by training models on past e-health data. This proactive strategy improves the system's capacity to successfully address new threats. Extensive experiments were carried out employing a broad dataset made up of real-world e-health scenarios to verify the efficacy of the suggested approach. The findings showed a marked improvement in the protection of confidentiality and privacy, along with a considerable decline in security breaches and unauthorised access events

Differentially Private Mixture of Generative Neural Networks

Generative models are used in a wide range of applications building on large amounts of contextually rich information. Due to possible privacy violations of the individuals whose data is used to train these models, however, publishing or sharing generative models is not always viable. In this paper, we present a novel technique for privately releasing generative models and entire high-dimensional datasets produced by these models. We model the generator distribution of the training data with a mixture of $k$ generative neural networks. These are trained together and collectively learn the generator distribution of a dataset. Data is divided into $k$ clusters, using a novel differentially private kernel $k$-means, then each cluster is given to separate generative neural networks, such as Restricted Boltzmann Machines or Variational Autoencoders, which are trained only on their own cluster using differentially private gradient descent. We evaluate our approach using the MNIST dataset, as well as call detail records and transit datasets, showing that it produces realistic synthetic samples, which can also be used to accurately compute arbitrary number of counting queries.Comment: A shorter version of this paper appeared at the 17th IEEE International Conference on Data Mining (ICDM 2017). This is the full version, published in IEEE Transactions on Knowledge and Data Engineering (TKDE

A comprehensive meta-analysis of cryptographic security mechanisms for cloud computing

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The concept of cloud computing offers measurable computational or information resources as a service over the Internet. The major motivation behind the cloud setup is economic benefits, because it assures the reduction in expenditure for operational and infrastructural purposes. To transform it into a reality there are some impediments and hurdles which are required to be tackled, most profound of which are security, privacy and reliability issues. As the user data is revealed to the cloud, it departs the protection-sphere of the data owner. However, this brings partly new security and privacy concerns. This work focuses on these issues related to various cloud services and deployment models by spotlighting their major challenges. While the classical cryptography is an ancient discipline, modern cryptography, which has been mostly developed in the last few decades, is the subject of study which needs to be implemented so as to ensure strong security and privacy mechanisms in today’s real-world scenarios. The technological solutions, short and long term research goals of the cloud security will be described and addressed using various classical cryptographic mechanisms as well as modern ones. This work explores the new directions in cloud computing security, while highlighting the correct selection of these fundamental technologies from cryptographic point of view

Security and trust in cloud computing and IoT through applying obfuscation, diversification, and trusted computing technologies

Cloud computing and Internet of Things (IoT) are very widely spread and commonly used technologies nowadays. The advanced services offered by cloud computing have made it a highly demanded technology. Enterprises and businesses are more and more relying on the cloud to deliver services to their customers. The prevalent use of cloud means that more data is stored outside the organization’s premises, which raises concerns about the security and privacy of the stored and processed data. This highlights the significance of effective security practices to secure the cloud infrastructure. The number of IoT devices is growing rapidly and the technology is being employed in a wide range of sectors including smart healthcare, industry automation, and smart environments. These devices collect and exchange a great deal of information, some of which may contain critical and personal data of the users of the device. Hence, it is highly significant to protect the collected and shared data over the network; notwithstanding, the studies signify that attacks on these devices are increasing, while a high percentage of IoT devices lack proper security measures to protect the devices, the data, and the privacy of the users. In this dissertation, we study the security of cloud computing and IoT and propose software-based security approaches supported by the hardware-based technologies to provide robust measures for enhancing the security of these environments. To achieve this goal, we use obfuscation and diversification as the potential software security techniques. Code obfuscation protects the software from malicious reverse engineering and diversification mitigates the risk of large-scale exploits. We study trusted computing and Trusted Execution Environments (TEE) as the hardware-based security solutions. Trusted Platform Module (TPM) provides security and trust through a hardware root of trust, and assures the integrity of a platform. We also study Intel SGX which is a TEE solution that guarantees the integrity and confidentiality of the code and data loaded onto its protected container, enclave. More precisely, through obfuscation and diversification of the operating systems and APIs of the IoT devices, we secure them at the application level, and by obfuscation and diversification of the communication protocols, we protect the communication of data between them at the network level. For securing the cloud computing, we employ obfuscation and diversification techniques for securing the cloud computing software at the client-side. For an enhanced level of security, we employ hardware-based security solutions, TPM and SGX. These solutions, in addition to security, ensure layered trust in various layers from hardware to the application. As the result of this PhD research, this dissertation addresses a number of security risks targeting IoT and cloud computing through the delivered publications and presents a brief outlook on the future research directions.Pilvilaskenta ja esineiden internet ovat nykyään hyvin tavallisia ja laajasti sovellettuja tekniikkoja. Pilvilaskennan pitkälle kehittyneet palvelut ovat tehneet siitä hyvin kysytyn teknologian. Yritykset enenevässä määrin nojaavat pilviteknologiaan toteuttaessaan palveluita asiakkailleen. Vallitsevassa pilviteknologian soveltamistilanteessa yritykset ulkoistavat tietojensa käsittelyä yrityksen ulkopuolelle, minkä voidaan nähdä nostavan esiin huolia taltioitavan ja käsiteltävän tiedon turvallisuudesta ja yksityisyydestä. Tämä korostaa tehokkaiden turvallisuusratkaisujen merkitystä osana pilvi-infrastruktuurin turvaamista. Esineiden internet -laitteiden lukumäärä on nopeasti kasvanut. Teknologiana sitä sovelletaan laajasti monilla sektoreilla, kuten älykkäässä terveydenhuollossa, teollisuusautomaatiossa ja älytiloissa. Sellaiset laitteet keräävät ja välittävät suuria määriä informaatiota, joka voi sisältää laitteiden käyttäjien kannalta kriittistä ja yksityistä tietoa. Tästä syystä johtuen on erittäin merkityksellistä suojata verkon yli kerättävää ja jaettavaa tietoa. Monet tutkimukset osoittavat esineiden internet -laitteisiin kohdistuvien tietoturvahyökkäysten määrän olevan nousussa, ja samaan aikaan suuri osuus näistä laitteista ei omaa kunnollisia teknisiä ominaisuuksia itse laitteiden tai niiden käyttäjien yksityisen tiedon suojaamiseksi. Tässä väitöskirjassa tutkitaan pilvilaskennan sekä esineiden internetin tietoturvaa ja esitetään ohjelmistopohjaisia tietoturvalähestymistapoja turvautumalla osittain laitteistopohjaisiin teknologioihin. Esitetyt lähestymistavat tarjoavat vankkoja keinoja tietoturvallisuuden kohentamiseksi näissä konteksteissa. Tämän saavuttamiseksi työssä sovelletaan obfuskaatiota ja diversifiointia potentiaalisiana ohjelmistopohjaisina tietoturvatekniikkoina. Suoritettavan koodin obfuskointi suojaa pahantahtoiselta ohjelmiston takaisinmallinnukselta ja diversifiointi torjuu tietoturva-aukkojen laaja-alaisen hyödyntämisen riskiä. Väitöskirjatyössä tutkitaan luotettua laskentaa ja luotettavan laskennan suoritusalustoja laitteistopohjaisina tietoturvaratkaisuina. TPM (Trusted Platform Module) tarjoaa turvallisuutta ja luottamuksellisuutta rakentuen laitteistopohjaiseen luottamukseen. Pyrkimyksenä on taata suoritusalustan eheys. Työssä tutkitaan myös Intel SGX:ää yhtenä luotettavan suorituksen suoritusalustana, joka takaa suoritettavan koodin ja datan eheyden sekä luottamuksellisuuden pohjautuen suojatun säiliön, saarekkeen, tekniseen toteutukseen. Tarkemmin ilmaistuna työssä turvataan käyttöjärjestelmä- ja sovellusrajapintatasojen obfuskaation ja diversifioinnin kautta esineiden internet -laitteiden ohjelmistokerrosta. Soveltamalla samoja tekniikoita protokollakerrokseen, työssä suojataan laitteiden välistä tiedonvaihtoa verkkotasolla. Pilvilaskennan turvaamiseksi työssä sovelletaan obfuskaatio ja diversifiointitekniikoita asiakaspuolen ohjelmistoratkaisuihin. Vankemman tietoturvallisuuden saavuttamiseksi työssä hyödynnetään laitteistopohjaisia TPM- ja SGX-ratkaisuja. Tietoturvallisuuden lisäksi nämä ratkaisut tarjoavat monikerroksisen luottamuksen rakentuen laitteistotasolta ohjelmistokerrokseen asti. Tämän väitöskirjatutkimustyön tuloksena, osajulkaisuiden kautta, vastataan moniin esineiden internet -laitteisiin ja pilvilaskentaan kohdistuviin tietoturvauhkiin. Työssä esitetään myös näkemyksiä jatkotutkimusaiheista

Overcoming Cloud Concerns with Trusted Execution Environments? Exploring the Organizational Perception of a Novel Security Technology in Regulated Swiss Companies

Trusted execution environments are a new approach for isolating data, specific parts of code, or an entire application within untrusted cloud environments. This emerging security technology could also enable the migration to cloud infrastructures for organizations working with highly sensitive data. As current research does not address the organizational perception of trusted execution environments (TEEs), we conducted an explorative study to clarify the technological, environmental, and organizational views on this technology by health care, life sciences, and banking companies in Switzerland. The interview findings show that in these industries, missing technological knowledge as well as privacy and process regulation are perceived to be the most critical driver for organizational adoption of TEEs. The identified low intrinsic motivation to adopt novel technologies permits us to conclude that clarifying the regulatory impact of TEEs could drive future adoption by organizations