C-ABSC : Cooperative Attribute Based SignCryption Scheme for Internet of Things applications

Belguith, S ORCID: https://orcid.org/0000-0003-0069-8552, Kaaniche, N, Mohamed, M and Russello, G 2018, C-ABSC : Cooperative Attribute Based SignCryption Scheme for Internet of Things applications , in: 2018 IEEE International Conference on Services Computing, 2nd-7th July 2018, San Francisco, CA, USA.

[img] PDF - Published Version
Restricted to Repository staff only

Download (182kB)

Abstract

In this paper, we present C-ABSC, a cooperative privacy preserving attribute based signcryption mechanism. It consists on performing the combined signing and encrypting processes of a set of data devices’ inputs in a secure collaborative manner. The main idea behind C-ABSC relies on the distribution of the signcrypting operation among different devices, with respect to selected sub-sets of a general access predicate, such as an untrusted aggregating entity is capable of decrypting the received aggregated data only if a sufficient number of IoT devices cooperates. The C-ABSC scheme is multifold. First, it provides a selective access to authenticated aggregated data contents. Second, it provides a privacy preserving signcrypting process, such that a curious aggregator can neither infer the used IoT device’s attributes for signing nor deciphering single data chunks. Third, C-ABSC relies on low computation and communication processes, mainly for resource-constrained devices.

Item Type: Conference or Workshop Item (Paper)
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title: 2018 IEEE International Conference on Services Computing (SCC)
Publisher: IEEE
ISBN: 9781538672501 (online)
ISSN: 2474-2473
Related URLs:
Depositing User: Dr. Sana Belguith
Date Deposited: 20 May 2019 11:11
Last Modified: 15 Feb 2022 17:22
URI: https://usir.salford.ac.uk/id/eprint/51368

Actions (login required)

Edit record (repository staff only) Edit record (repository staff only)

Downloads

Downloads per month over past year