Online advertising: analysis of privacy threats and protection approaches

Online advertising, the pillar of the “free” content on the Web, has revolutionized the marketing business in recent years by creating a myriad of new opportunities for advertisers to reach potential customers. The current advertising model builds upon an intricate infrastructure composed of a variety of intermediary entities and technologies whose main aim is to deliver personalized ads. For this purpose, a wealth of user data is collected, aggregated, processed and traded behind the scenes at an unprecedented rate. Despite the enormous value of online advertising, however, the intrusiveness and ubiquity of these practices prompt serious privacy concerns. This article surveys the online advertising infrastructure and its supporting technologies, and presents a thorough overview of the underlying privacy risks and the solutions that may mitigate them. We first analyze the threats and potential privacy attackers in this scenario of online advertising. In particular, we examine the main components of the advertising infrastructure in terms of tracking capabilities, data collection, aggregation level and privacy risk, and overview the tracking and data-sharing technologies employed by these components. Then, we conduct a comprehensive survey of the most relevant privacy mechanisms, and classify and compare them on the basis of their privacy guarantees and impact on the Web.Peer ReviewedPostprint (author's final draft

Control-data separation architecture for cellular radio access networks: a survey and outlook

Conventional cellular systems are designed to ensure ubiquitous coverage with an always present wireless channel irrespective of the spatial and temporal demand of service. This approach raises several problems due to the tight coupling between network and data access points, as well as the paradigm shift towards data-oriented services, heterogeneous deployments and network densification. A logical separation between control and data planes is seen as a promising solution that could overcome these issues, by providing data services under the umbrella of a coverage layer. This article presents a holistic survey of existing literature on the control-data separation architecture (CDSA) for cellular radio access networks. As a starting point, we discuss the fundamentals, concepts, and general structure of the CDSA. Then, we point out limitations of the conventional architecture in futuristic deployment scenarios. In addition, we present and critically discuss the work that has been done to investigate potential benefits of the CDSA, as well as its technical challenges and enabling technologies. Finally, an overview of standardisation proposals related to this research vision is provided

Scalable RAN Virtualization in Multi-Tenant LTE-A Heterogeneous Networks (Extended version)

Cellular communications are evolving to facilitate the current and expected increasing needs of Quality of Service (QoS), high data rates and diversity of offered services. Towards this direction, Radio Access Network (RAN) virtualization aims at providing solutions of mapping virtual network elements onto radio resources of the existing physical network. This paper proposes the Resources nEgotiation for NEtwork Virtualization (RENEV) algorithm, suitable for application in Heterogeneous Networks (HetNets) in Long Term Evolution-Advanced (LTE-A) environments, consisting of a macro evolved NodeB (eNB) overlaid with small cells. By exploiting Radio Resource Management (RRM) principles, RENEV achieves slicing and on demand delivery of resources. Leveraging the multi-tenancy approach, radio resources are transferred in terms of physical radio Resource Blocks (RBs) among multiple heterogeneous base stations, interconnected via the X2 interface. The main target is to deal with traffic variations in geographical dimension. All signaling design considerations under the current Third Generation Partnership Project (3GPP) LTE-A architecture are also investigated. Analytical studies and simulation experiments are conducted to evaluate RENEV in terms of network's throughput as well as its additional signaling overhead. Moreover we show that RENEV can be applied independently on top of already proposed schemes for RAN virtualization to improve their performance. The results indicate that significant merits are achieved both from network's and users' perspective as well as that it is a scalable solution for different number of small cells.Comment: 40 pages (including Appendices), Accepted for publication in the IEEE Transactions on Vehicular Technolog

Will SDN be part of 5G?

For many, this is no longer a valid question and the case is considered settled with SDN/NFV (Software Defined Networking/Network Function Virtualization) providing the inevitable innovation enablers solving many outstanding management issues regarding 5G. However, given the monumental task of softwarization of radio access network (RAN) while 5G is just around the corner and some companies have started unveiling their 5G equipment already, the concern is very realistic that we may only see some point solutions involving SDN technology instead of a fully SDN-enabled RAN. This survey paper identifies all important obstacles in the way and looks at the state of the art of the relevant solutions. This survey is different from the previous surveys on SDN-based RAN as it focuses on the salient problems and discusses solutions proposed within and outside SDN literature. Our main focus is on fronthaul, backward compatibility, supposedly disruptive nature of SDN deployment, business cases and monetization of SDN related upgrades, latency of general purpose processors (GPP), and additional security vulnerabilities, softwarization brings along to the RAN. We have also provided a summary of the architectural developments in SDN-based RAN landscape as not all work can be covered under the focused issues. This paper provides a comprehensive survey on the state of the art of SDN-based RAN and clearly points out the gaps in the technology.Comment: 33 pages, 10 figure