Biblio
Named data network (NDN) is one of the most promising information-centric networking architectures, where the core concept is to focus on the named data (or contents) themselves. Users in NDN can easily send a request packet to get the desired content regardless of its address. The routers in NDN have cache functionality to make the users instantly retrieve the desired file. Thus, the user can immediately get the desired file from the nearby nodes instead of the remote host. Nevertheless, NDN is a novel proposal and there are still some open issues to be resolved. In view of previous research, it is a challenge to achieve access control on a specific user and support potential receivers simultaneously. In order to solve it, we present a fine-grained access control mechanism tailored for NDN, supporting data confidentiality, potential receivers, and mobility. Compared to previous works, this is the first to support fine-grained access control and potential receivers. Furthermore, the proposed scheme achieves provable security under the DBDH assumption.
Delegated Proof-of-Stake (DPoS) is an efficient, decentralized, and flexible consensus framework available in the blockchain industry. However, applying DPoS to the decentralized Internet of Things (IoT) applications is quite challenging due to the nature of IoT systems such as large-scale deployments and huge amount of data. To address the unique challenge for IoT based blockchain applications, we present Roll-DPoS, a randomized delegated proof of stake algorithm. Roll-DPoS inherits all the advantages of the original DPoS consensus framework and further enhances its capability in terms of decentralization as well as extensibility to complex blockchain architectures. A number of modern cryptographic techniques have been utilized to optimize the consensus process with respect to the computational and communication overhead.
The paper deals with the implementation aspects of the bilinear pairing operation over an elliptic curve on constrained devices, such as smart cards, embedded devices, smart meters and similar devices. Although cryptographic constructions, such as group signatures, anonymous credentials or identity-based encryption schemes, often rely on the pairing operation, the implementation of such schemes into practical applications is not straightforward, in fact, it may become very difficult. In this paper, we show that the implementation is difficult not only due to the high computational complexity, but also due to the lack of cryptographic libraries and programming interfaces. In particular, we show how difficult it is to implement pairing-based schemes on constrained devices and show the performance of various libraries on different platforms. Furthermore, we show the performance estimates of fundamental cryptographic constructions, the group signatures. The purpose of this paper is to reduce the gap between the cryptographic designers and developers and give performance results that can be used for the estimation of the implementability and performance of novel, upcoming schemes.
The objective of this paper is to outline the design specification, implementation and evaluation of a proposed accelerated encryption framework which deploys both homomorphic and symmetric-key encryptions to serve the privacy preserving processing; in particular, as a sub-system within the Privacy Preserving Speech Processing framework architecture as part of the PPSP-in-Cloud Platform. Following a preliminary study of GPU efficiency gains optimisations benchmarked for AES implementation we have addressed and resolved the Big Integer processing challenges in parallel implementation of bilinear pairing thus enabling the creation of partially homomorphic encryption schemes which facilitates applications such as speech processing in the encrypted domain on the cloud. This novel implementation has been validated in laboratory tests using a standard speech corpus and can be used for other application domains to support secure computation and privacy preserving big data storage/processing in the cloud.
Big data is the next frontier for modernization, rivalry, and profitability. It is the foundation of all the major trends such as social networks, mobile devices, healthcare, stock markets etc. Big data is efficiently stored in the cloud because of its high-volume, high-speed and high-assortment data resources. An unauthorized user access control is the gravest threat of huge information in the cloud environment because of the remote file storage. Attribute Based Encryption (ABE) is an efficient access control procedure to guarantee end-to-end security for huge information in the cloud. Most often existing ABE working principle is based on bilinear pairing. In this paper, we construct a peculiar ABE for big data in the cloud. Our proposed scheme is based on quadratic residue and attribute union which is based on fundamental arithmetic theorem.