Abstract
Current methods for optimization of low-density parity-check (LDPC) codes analyze the degree distribution pair asymptotically as block length approaches infinity. This effectively ignores the discrete nature of the space of valid degree distribution pairs for LDPC codes of finite block length. While large codes are likely to conform reasonably well to the infinite block length analysis, shorter codes have no such guarantee. We present and analyze an algorithm for completely enumerating the space of all valid degree distribution pairs for a given block length, code rate, maximum variable node degree, and maximum check node degree. We then demonstrate this algorithm on an example LDPC code of finite block length. Finally, we discuss how the result of this algorithm can be utilized by discrete optimization routines to form novel methods for the optimization of small block length LDPC codes.

Abstract
Due to the pervasiveness of Interconnected devices, large amounts of heterogeneous data types are being continuously collected. Regardless of the benefits that come from sharing data, exposing sensitive and private information arises serious privacy concerns. To prevent unwanted disclosures and, hence, to protect users' privacy, several privacy-preserving mechanisms have been proposed. However, the data heterogeneity and the inherent correlations among the different data types have been disregarded when developing such mechanisms. Our goal is to develop privacy-preserving mechanisms that are suitable for data heterogeneity and data correlation. These aspects will also be considered to develop mechanisms to achieve private learning. © 2021 Owner/Author.

Abstract
The evolutionary growth of information technology has enabled us with platforms that eases access to a wide range of electronic services. Typically, access to these services requires users to authenticate their identity, which involves the release, dissemination and processing of personal data by third parties such as service and identity providers. The involvement of these and other entities in managing and processing personal identifiable data has continued to raise concerns on privacy of personal information. Identity management systems (IdMs) emerged as a promising solution to address major access control and privacy issues, however most research works are focused on securing service providers (SPs) and the services provided, with little emphases on users privacy. In order to optimise users privacy and ensure that personal information are used only for intended purposes, there is need for authorisation systems that controls who may access what and under what conditions. However, for adoption data owners perspective must not be neglected. To address these issues, this paper introduces the concept of IdM and access control framework which operates with RESTful based services. The proposal provides a new level of abstraction and logic in access management, while giving data owner a decisive control over access to personal data using smartphone. The framework utilises Attribute based access control (ABAC) method to authenticate and authorise users, Open ID Connect (OIDC) protocol for data owner authorisation and Public-key cryptography to achieve perfect forward secrecy communication. The solution enables data owner to attain the responsibility of granting or denying access to their data, from a secured communication with an identity provider using a digitally signed token.

Abstract
Payments using cryptocurrencies may require that the user is able to provide proof of ownership and proof of provenance for a specific transaction. In this paper an innovative web based solution is proposed as a framework that issues reports, on request, pertaining proof of ownership and proof of provenance. The proposed framework provides proof of ownership by using micro-payments and, when used recursively, it can produce provenance reports up to a defined granularity level of transactions. A proof of concept prototype of the proposed framework was implemented and its operation and output is presented and explained. Some limitations and future work directions are also identified.

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