Accelerated Encryption Algorithms for Secure Storage and Processing in the Cloud
| Title | Accelerated Encryption Algorithms for Secure Storage and Processing in the Cloud |
| Publication Type | Conference Paper |
| Year of Publication | 2017 |
| Authors | Badii, A., Faulkner, R., Raval, R., Glackin, C., Chollet, G. |
| Conference Name | 2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) |
| ISBN Number | 978-1-5386-0551-6 |
| Keywords | accelerated encryption algorithms, accelerated encryption framework, Acceleration, AES, AES implementation, Big Data, big data security in the cloud, Big Data storage/processing, Bilinear Pairing, Ciphers, cloud computing, cloud processing security, cloud storage security, cryptography, data privacy, design specification, Encryption, gpu computing, graphics processing units, homomorphic encryption, Metrics, parallel processing, partially homomorphic encryption schemes, PPSP-in-Cloud Platform, Privacy Preserving Speech Processing, privacy preserving speech processing framework architecture, pubcrawl, Resiliency, Scalability, secure computation, speech processing, storage management, Symmetric-Key Cryptography, symmetric-key encryptions |
| Abstract | 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. |
| URL | http://ieeexplore.ieee.org/document/8075572/ |
| DOI | 10.1109/ATSIP.2017.8075572 |
| Citation Key | badii_accelerated_2017 |
- pubcrawl
- graphics processing units
- Homomorphic encryption
- Metrics
- parallel processing
- partially homomorphic encryption schemes
- PPSP-in-Cloud Platform
- Privacy Preserving Speech Processing
- privacy preserving speech processing framework architecture
- gpu computing
- Resiliency
- Scalability
- Secure computation
- speech processing
- storage management
- Symmetric-Key Cryptography
- symmetric-key encryptions
- Ciphers
- accelerated encryption framework
- Acceleration
- AES
- AES implementation
- Big Data
- big data security in the cloud
- Big Data storage/processing
- Bilinear Pairing
- accelerated encryption algorithms
- Cloud Computing
- cloud processing security
- cloud storage security
- Cryptography
- data privacy
- design specification
- encryption