Biblio

Currently, little attention has been carried out to address the feasibility of in-network caching in Information-Centric Networking (ICN) for the design and real-world deployment of future networks. Towards this line, in this paper, we propose a beneficial caching scheme in ICN by storing no more than a specific number of replicas for each content. Particularly, to realize an optimal content distribution for deploying caches in ICN, a content can be cached either partially or as a full-object corresponding to its request arrival rate and data traffic. Also, we employ a utility-based replacement in each content node to keep the most recent and popular content items in the ICN interconnections. The evaluation results show that the proposal improves the cache hit rate and cache diversity considerably, and acts as a beneficial caching approach for network and service providers in ICN. Specifically, the proposed caching mechanism is easy to deploy, robust, and relevant for the content-based providers by enabling them to offer users high Quality of Service (QoS) and gain benefits at the same time.

Despite the latest initiatives and research efforts to increase user privacy in digital scenarios, identity-related cybercrimes such as identity theft, wrong identity or user transactions surveillance are growing. In particular, blanket surveillance that might be potentially accomplished by Identity Providers (IdPs) contradicts the data minimization principle laid out in GDPR. Hence, user movements across Service Providers (SPs) might be tracked by malicious IdPs that become a central dominant entity, as well as a single point of failure in terms of privacy and security, putting users at risk when compromised. To cope with this issue, the OLYMPUS H2020 EU project is devising a truly privacy-preserving, yet user-friendly, and distributed identity management system that addresses the data minimization challenge in both online and offline scenarios. Thus, OLYMPUS divides the role of the IdP among various authorities by relying on threshold cryptography, thereby preventing user impersonation and surveillance from malicious or nosy IdPs. This paper overviews the OLYMPUS framework, including requirements considered, the proposed architecture, a series of use cases as well as the privacy analysis from the legal point of view.

This research provides security and safety extensions to a blockchain based solution whose target is e-health. The Advanced Blockchain platform is extended with intelligent monitoring for security and machine learning for detecting patient treatment medication safety issues. For the reasons of stringent HIPAA, HITECH, EU-GDPR and other regional regulations dictating security, safety and privacy requirements, the e-Health blockchains have to cover mandatory disclosure of violations or enforcements of policies during transaction flows involving healthcare. Our service solution further provides the benefits of resolving the abnormal flows of a medical treatment process, providing accountability of the service providers, enabling a trust health information environment for institutions to handle medication safely, giving patients a better safety guarantee, and enabling the authorities to supervise the security and safety of e-Health blockchains. The capabilities can be generalized to support a uniform smart solution across industry in a variety of blockchain applications.

Service providers typically utilize Web APIs to enable the sharing of tenant data and resources with numerous third party web, cloud, and mobile applications. Security mechanisms such as OAuth 2.0 and API keys are commonly applied to manage authorization aspects of such integrations. However, these mechanisms impose functional and security drawbacks both for service providers and their users due to their static design, coarse and context insensitive capabilities, and weak interoperability. Implementing secure, feature-rich, and flexible data sharing services still poses a challenge that many providers face in the process of opening their interfaces to the public.To address these issues, we design the framework that allows pluggable and transparent externalization of authorization functionality for service providers and flexibility in defining and managing security aspects of resource sharing with third parties for their users. Our solution applies a holistic perspective that considers service descriptions, data fragments, security policies, as well as system interactions and states as an integrated space dynamically exposed and collaboratively accessed by agents residing across organizational boundaries.In this work we present design aspects of our contribution and illustrate its practical implementation by analyzing case scenario involving resource sharing of a popular service.

In recent years, service providers, such as mobile operators providing wireless services, collected location data in enormous extent with the increase of the usages of mobile phones. Vertical businesses, such as banks, may want to use this location information for their own scenarios. However, service providers cannot directly provide these private data to the vertical businesses because of the privacy and legal issues. In this demo, we show how privacy preserving solutions can be utilized using such location-based queries without revealing each organization's sensitive data. In our demonstration, we used partially homomorphic cryptosystem in our protocols and showed practicality and feasibility of our proposed solution.

Wearable smart sensing devices presently become more and more popular in people's daily life, which also brings serious problems related to personal data privacy. In order to provide users better experiences, wearable smart sensing devices are collecting users' personal data all the time and uploading the data to service provider to get computing services, which objectively let service provider master each user's condition and cause a lot of problems such as spam, harassing call, etc. This paper designs a blockchain based scheme to solve such problems by cutting off the association between user identifier and its sensing data from perspective of shielding service providers and adversaries. Firstly, privacy requirements and situations in smart sensing area are reviewed. Then, three key technologies are introduced in the scheme including its theories, purposes and usage. Next, the designed protocol is shown and analyzed in detail. Finally, security analysis and engineering feasibility of the scheme are given. This scheme will give user better experience from privacy protection perspective in smart sensing area.

In this paper, an agent-based cross-layer secure service discovery scheme has been presented. Service discovery in MANET is a critical task and it presents numerous security challenges. These threats can compromise the availability, privacy and integrity of service discovery process and infrastructure. This paper highlights various security challenges prevalent to service discovery in MANET. Then, in order to address these security challenges, the paper proposes a cross-layer, agent based secure service discovery scheme for MANET based on deep neural network. The software agents will monitor the intrusive activities in the network based on an Intrusion Detection System (IDS). The service discovery operation is performed based on periodic dissemination of service, routing and security information. The QoS provisioning is achieved by encapsulating QoS information in the periodic advertisements done by service providers. The proposed approach has been implemented in JIST/ SWANS simulator. The results show that proposed approach provides improved security, scalability, latency, packet delivery ratio and service discovery success ratio, for various simulation scenarios.

Identity management is an essential cornerstone of securing online services. Service provisioning relies on correct and valid attributes of a digital identity. Therefore, the identity provider is a trusted third party with a specific trust requirement towards a verified attribute supply. This trust demand implies a significant dependency on users and service providers. We propose a novel attribute aggregation method to reduce the reliance on one identity provider. Trust in an attribute is modelled as a combined assurance of several identity providers based on probability distributions. We formally describe the proposed aggregation model. The resulting trust model is implemented in a gateway that is used for authentication with self-sovereign identity solutions. Thereby, we devise a service provider specific web of trust that constitutes an intermediate approach bridging a global hierarchical model and a locally decentralized peer to peer scheme.

Once an individual employs the use of the Internet for accessing information; carrying out transactions and sharing of data on the Cloud, they are connected to diverse computers on the network. As such, security of such transmitted data is most threatened and then potentially creating privacy risks of users on the federated identity management system in the Cloud. Usually, User's attributes or Personal Identifiable Information (PII) are needed to access Services on the Cloud from different Service Providers (SPs). Sometime these SPs may by themselves violate user's privacy by the reuse of user's attributes offered them for the release of services to the users without their consent and then carrying out activities that may appear malicious and then causing damage to the users. Similarly, it should be noted that sensitive user's attributes (e.g. first name, email, address and the likes) are received in their original form by needed SPs in plaintext. As a result of these problems, user's privacy is being violated. Since these SPs may reuse them or connive with other SPs to expose a user's identity in the cloud environment. This research is motivated to provide a protective and novel approach that shall no longer release original user's attributes to SPs but pseudonyms that shall prevent the SPs from violating user's privacy through connivance to expose the user's identity or other means. The paper introduces a conceptual framework for the proposed user's attributes privacy protection in a federated identity management system for the cloud. On the proposed system, the use of pseudonymous technique also called Privacy Token (PT) is employed. The pseudonymous technique ensures users' original attributes values are not sent directly to the SP but auto generated pseudo attributes values. The PT is composed of: Pseudo Attribute values, Timestamp and SPİD. These composition of the PT makes it difficult for the User's PII to be revealed and further preventing the SPs from being able to keep them or reuse them in the future without the user's consent for any purpose. Another important feature of the PT is its ability to forestall collusion among several collaborating service providers. This is due to the fact that each SP receives pseudo values that have no direct link to the identity of the user. The prototype was implemented with Java programming language and its performance tested on CloudAnalyst simulation.

A smart environment is a physical space where devices are connected to provide continuous support to individuals and make their life more comfortable. For this purpose, a smart environment collects, stores, and processes a massive amount of personal data. In general, service providers collect these data according to their privacy policies. To enhance the privacy control, individuals can explicitly express their privacy preferences, stating conditions on how their data have to be used and managed. Typically, privacy checking is handled through the hard matching of users' privacy preferences against service providers' privacy policies, by denying all service requests whose privacy policies do not fully match with individual's privacy preferences. However, this hard matching might be too restrictive in a smart environment because it denies the services that partially satisfy the individual's privacy preferences. To cope with this challenge, in this paper, we propose a soft privacy matching mechanism, able to relax, in a controlled way, some conditions of users' privacy preferences such to match with service providers' privacy policies. At this aim, we exploit machine learning algorithms to build a classifier, which is able to make decisions on future service requests, by learning which privacy preference components a user is prone to relax, as well as the relaxation tolerance. We test our approach on two realistic datasets, obtaining promising results.

The growth in Internet usage has increased the use of electronic services requiring users to register their identity on each service they subscribe to. This has resulted in the prevalence of redundant users data on different services. To protect and regulate access by users to these services identity management systems (IdMs)are put in place. IdMs uses frameworks and standards e.g SAML, OAuth and Shibboleth to manage digital identities of users for identification and authentication process for a service provider. However, current IdMs have not been able to address privacy issues (unauthorised and fine-grained access)that relate to protecting users identity and private data on web services. Many implementations of these frameworks are only concerned with the identification and authentication process of users but not authorisation. They mostly give full control of users digital identities and data to identity and service providers with less or no users participation. This results in a less privacy enhanced solutions that manage users available data in the electronic space. This article proposes a user-centred mandate representation system that empowers resource owners to take full of their digital data; determine and delegate access rights using their mobile phone. Thereby giving users autonomous powers on their resources to grant access to authenticated entities at their will. Our solution is based on the OpenID Connect framework for authorisation service. To evaluate the proposal, we've compared it with some related works and the privacy requirements yardstick outlined in GDPR regulation [1] and [2]. Compared to other systems that use OAuth 2.0 or SAML our solution uses an additional layer of security, where data owner assumes full control over the disclosure of their identity data through an assertion issued from their mobile phones to authorisation server (AS), which in turn issues an access token. This would enable data owners to assert the authenticity of a request, while service providers and requestors also benefit from the correctness and freshness of identity data disclosed to them.

Security is a key concern in Internet of Things (IoT) designs. In a heterogeneous and complex environment, service providers and service requesters must trust each other. On-off attack is a sophisticated trust threat in which a malicious device can perform good and bad services randomly to avoid being rated as a low trust node. Some countermeasures demands prior level of trust knowing and time to classify a node behavior. In this paper, we introduce a Smart Middleware that automatically assesses the IoT resources trust, evaluating service providers attributes to protect against On-off attacks.

Conversational IT services are expected to reduce user wait times and improve overall customer satisfaction. Cloud-based solutions are readily available for enterprise subject matter experts (SMEs) to train user-question classifiers and build conversational services with little effort. However, methodologies that the SMEs can use to improve the response accuracy and conversation quality are merely stated and evaluated. In complex service scenarios such as software support, the scope of topics is typically large and the training samples are often limited. Thus, training the classifier based on labeled samples of plain user utterances is not effective in most cases. In this paper, we identify several methods for improving classification quality and evaluate them in concrete training set scenarios. Particularly, a process-based methodology is described that builds and refines on top of service domain knowledge in order to develop a scalable solution for training accurate conversation services. Enterprises and service providers are continuously seeking new ways to improve customer experience on working with IT systems, where user wait times and service resolution quality are critical business metrics. One of the latest trends is the use of conversational IT services. Customers can interact with a conversational service to express their questions in natural language and the system can automatically return relevant answers or execute back-end processes for automated actions. Various text classification techniques have been developed and applied to understand the user questions and trigger the correct responses. For instance, in the context of IT software support, customers can use conversational systems to get answers about software product errors, licenses, or upgrade processes. While the potential benefits of building conversational services are huge, it is often difficult to effectively train classification models that cover well the scope of realistically complex services. In this paper, we propose a training methodology that addresses the limitations in both the scope of topics and the scarcity of the training set. We further evaluate the proposed methodology in a real service support scenario and share the lessons learned.

Internet of Things (IoT) distributed secure data management system is characterized by authentication, privacy policies to preserve data integrity. Multi-phase security and privacy policies ensure confidentiality and trust between the users and service providers. In this regard, we present a novel Two-phase Incentive-based Secure Key (TISK) system for distributed data management in IoT. The proposed system classifies the IoT user nodes and assigns low-level, high-level security keys for data transactions. Low-level secure keys are generic light-weight keys used by the data collector nodes and data aggregator nodes for trusted transactions. TISK phase-I Generic Service Manager (GSM-C) module verifies the IoT devices based on self-trust incentive and server-trust incentive levels. High-level secure keys are dedicated special purpose keys utilized by data manager nodes and data expert nodes for authorized transactions. TISK phase-II Dedicated Service Manager (DSM-C) module verifies the certificates issued by GSM-C module. DSM-C module further issues high-level secure keys to data manager nodes and data expert nodes for specific purpose transactions. Simulation results indicate that the proposed TISK system reduces the key complexity and key cost to ensure distributed secure data management in IoT network.

Traffic from mobile wireless networks has been growing at a fast pace in recent years and is expected to surpass wired traffic very soon. Service providers face significant challenges at such scales including providing seamless mobility, efficient data delivery, security, and provisioning capacity at the wireless edge. In the Mobility First project, we have been exploring clean slate enhancements to the network protocols that can inherently provide support for at-scale mobility and trustworthiness in the Internet. An extensible data plane using pluggable compute-layer services is a key component of this architecture. We believe these extensions can be used to implement in-network services to enhance mobile end-user experience by either off-loading work and/or traffic from mobile devices, or by enabling en-route service-adaptation through context-awareness (e.g., Knowing contemporary access bandwidth). In this work we present details of the architectural support for in-network services within Mobility First, and propose protocol and service-API extensions to flexibly address these pluggable services from end-points. As a demonstrative example, we implement an in network service that does rate adaptation when delivering video streams to mobile devices that experience variable connection quality. We present details of our deployment and evaluation of the non-IP protocols along with compute-layer extensions on the GENI test bed, where we used a set of programmable nodes across 7 distributed sites to configure a Mobility First network with hosts, routers, and in-network compute services.

Anonymous communications networks, such as Tor, help to solve the real and important problem of enabling users to communicate privately over the Internet. However, in doing so, anonymous communications networks introduce an entirely new problem for the service providers - such as websites, IRC networks or mail servers - with which these users interact, in particular, since all anonymous users look alike, there is no way for the service providers to hold individual misbehaving anonymous users accountable for their actions. Recent research efforts have focused on using anonymous blacklisting systems (which are sometimes called anonymous revocation systems) to empower service providers with the ability to revoke access from abusive anonymous users. In contrast to revocable anonymity systems, which enable some trusted third party to deanonymize users, anonymous blacklisting systems provide users with a way to authenticate anonymously with a service provider, while enabling the service provider to revoke access from any users that misbehave, without revealing their identities. In this paper, we introduce the anonymous blacklisting problem and survey the literature on anonymous blacklisting systems, comparing and contrasting the architecture of various existing schemes, and discussing the tradeoffs inherent with each design. The literature on anonymous blacklisting systems lacks a unified set of definitions, each scheme operates under different trust assumptions and provides different security and privacy guarantees. Therefore, before we discuss the existing approaches in detail, we first propose a formal definition for anonymous blacklisting systems, and a set of security and privacy properties that these systems should possess. We also outline a set of new performance requirements that anonymous blacklisting systems should satisfy to maximize their potential for real-world adoption, and give formal definitions for several optional features already supported by some sche- - mes in the literature.