The last few years have witnessed a tremendous growth of the demand for wireless services and a significant increase of the number of mobile subscribers. A recent data traffic forecast from Cisco reported that the global mobile data traffic reached 1.2 zettabytes per year in 2016, and the global IP traffic will increase nearly threefold over the next 5 years. Based on these predictions, a 127-fold increase of the IP traffic is expected from 2005 to 2021. It is also anticipated that the mobile data traffic will reach 3.3 zettabytes per year by 2021, and that the number of mobile-connected devices will reach 3.5 per capita.
With such demands for higher data rates and for better quality of service (QoS), fifth generation (5G) standardization initiatives, whose initial phase was specified in June 2018 under the umbrella of Long Term Evolution (LTE) Release 15, have been under vibrant investigation. In particular, the International Telecommunication Union (ITU) has identified three usage scenarios (service categories) for 5G wireless networks: (i) enhanced mobile broadband (eMBB), (ii) ultra-reliable and low latency communications (uRLLC), and (iii) massive machine type communications (mMTC). The vast variety of applications for beyond 5G wireless networks has motivated the necessity of novel and more flexible physical layer (PHY) technologies, which are capable of providing higher spectral and energy efficiencies, as well as reduced transceiver implementations.
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Sayler, Andy (University of Colorado at Boulder) “Securing Secrets and Managing Trust in Modern Computing Applications” (2016) Advisor: Dirk Grunwald
The amount of digital data generated and stored by users increases every day. In order to protect this data, modern computing systems employ numerous cryptographic and access control solutions. Almost all of such solutions, however, require the keeping of certain secrets as the basis of their security models. How best to securely store and control access to these secrets is a significant challenge: such secrets must be stored in a manner that protects them from a variety of potentially malicious actors while still enabling the kinds of functionality users expect.
This dissertation discusses a system for isolating secrets from the applications that rely on them and storing these secrets via a standardized, service-oriented secret storage system. This “Secret Storage as a Service” (SSaaS) model allows users to reduce the trust they must place in any single actor while still providing mechanisms to support a range of cloud-based, multi-user, and multi-device use cases.
This dissertation contains the following contributions: an overview of the secret-storage problem and how it relates to the security and privacy of modern computing systems and users, a framework for evaluating the degree by which one must trust various actors across a range of popular use cases and the mechanisms by which this trust can be violated, a description of the SSaaS model and how it helps avoid such trust and security failures, a discussion of how the SSaaS approach can integrate with and improve the security of a range of applications, an overview of Custos – a first-generation SSaaS prototype, an overview of Tutamen – a next-generation SSaaS prototypes, and an exploration of the legal and policy implications of the SSaaS ecosystem.
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