Towards Real-Time Hidden Speaker Recognition by Means of Fully Homomorphic Encryption

HARDWARE IMPLEMENTATION OF AN ENCRYPTION FOR ENHANCEMENT DGHV

Iraqi Journal of Information & Communications Technology ◽

10.31987/ijict.2.2.69 ◽

2019 ◽

Vol 2 (2) ◽

pp. 44-57

Author(s):

Zainab H. Mahmood ◽

Mahmood K. Ibrahem

Keyword(s):

Real Time ◽

High Speed ◽

Hardware Implementation ◽

Graphics Processing Unit ◽

Homomorphic Encryption ◽

Processing Unit ◽

Fully Homomorphic Encryption ◽

Encryption Schemes ◽

Field Programmable ◽

Programmable Gate Arrays

In constructing a secure and reliable cloud computing environment, a fully homomorphic encryption (FHE) scheme is conceived as a major cryptographic tool, as it enables arbitrary arithmetic evaluation of a cipher text without revealing the plaintext. However, due to very high of fully homomorphic encryption systems stays impractical and unfit for real-time applications One way to address this restriction is by using graphics processing unit (GPUs) and field programmable gate arrays (FPGAs) to produce homomorphic encryption schemes. This paper represents the hardware implementation of an encryption for enhancement van Dijk, Gentry, Halevi and Vaikuntanathan’s (DGHV) scheme over the integer (DGHV10) using FPGA technology for high speed computation and real time results. The proposed method was simulated via Vivado system generator tools. Then design systems of fully homomrphic encryption are implemented in an FPGA hardware successfully using NEXYS 4 DDR board with ARTIX 7 XC7A100T FPGA. The Experimental results show that the FPGA- based fully homomorphic encryption system is 63 times faster than the simulation based implementation.

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A Neural Network Application of Fully Homomorphic Encryption for Cloud Computing

SSRN Electronic Journal ◽

10.2139/ssrn.3565268 ◽

2020 ◽

Author(s):

Megha Kolhekar ◽

Ashish Pandey ◽

Ayushi Raina ◽

Rijin Thomas ◽

Vaibhav Tiwari ◽

...

Keyword(s):

Neural Network ◽

Cloud Computing ◽

Homomorphic Encryption ◽

Fully Homomorphic Encryption ◽

Network Application

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A fully homomorphic encryption based on magic number fragmentation and El‐Gamal encryption: Smart healthcare use case

Expert Systems ◽

10.1111/exsy.12767 ◽

2021 ◽

Author(s):

Mostefa Kara ◽

Abdelkader Laouid ◽

Mohammed Amine Yagoub ◽

Reinhardt Euler ◽

Saci Medileh ◽

...

Keyword(s):

Homomorphic Encryption ◽

Magic Number ◽

Use Case ◽

Healthcare Use ◽

Fully Homomorphic Encryption ◽

Smart Healthcare

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Assurance monitoring of learning-enabled cyber-physical systems using inductive conformal prediction based on distance learning

Artificial intelligence for engineering design analysis and manufacturing ◽

10.1017/s089006042100010x ◽

2021 ◽

Vol 35 (2) ◽

pp. 251-264

Author(s):

Dimitrios Boursinos ◽

Xenofon Koutsoukos

Keyword(s):

Neural Networks ◽

Distance Learning ◽

Real Time ◽

Speaker Recognition ◽

Deep Neural Networks ◽

Cyber Physical Systems ◽

Error Rates ◽

Traffic Sign ◽

Conformal Prediction ◽

Physical Systems

AbstractMachine learning components such as deep neural networks are used extensively in cyber-physical systems (CPS). However, such components may introduce new types of hazards that can have disastrous consequences and need to be addressed for engineering trustworthy systems. Although deep neural networks offer advanced capabilities, they must be complemented by engineering methods and practices that allow effective integration in CPS. In this paper, we proposed an approach for assurance monitoring of learning-enabled CPS based on the conformal prediction framework. In order to allow real-time assurance monitoring, the approach employs distance learning to transform high-dimensional inputs into lower size embedding representations. By leveraging conformal prediction, the approach provides well-calibrated confidence and ensures a bounded small error rate while limiting the number of inputs for which an accurate prediction cannot be made. We demonstrate the approach using three datasets of mobile robot following a wall, speaker recognition, and traffic sign recognition. The experimental results demonstrate that the error rates are well-calibrated while the number of alarms is very small. Furthermore, the method is computationally efficient and allows real-time assurance monitoring of CPS.

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A Password Meter without Password Exposure

Sensors ◽

10.3390/s21020345 ◽

2021 ◽

Vol 21 (2) ◽

pp. 345

Author(s):

Pyung Kim ◽

Younho Lee ◽

Youn-Sik Hong ◽

Taekyoung Kwon

Keyword(s):

Performance Enhancement ◽

Homomorphic Encryption ◽

Optimization Techniques ◽

Third Party ◽

Fully Homomorphic Encryption ◽

Performance Achievement ◽

Server Side ◽

On Line ◽

Internal Mechanism ◽

High Chance

To meet password selection criteria of a server, a user occasionally needs to provide multiple choices of password candidates to an on-line password meter, but such user-chosen candidates tend to be derived from the user’s previous passwords—the meter may have a high chance to acquire information about a user’s passwords employed for various purposes. A third party password metering service may worsen this threat. In this paper, we first explore a new on-line password meter concept that does not necessitate the exposure of user’s passwords for evaluating user-chosen password candidates in the server side. Our basic idea is straightforward; to adapt fully homomorphic encryption (FHE) schemes to build such a system but its performance achievement is greatly challenging. Optimization techniques are necessary for performance achievement in practice. We employ various performance enhancement techniques and implement the NIST (National Institute of Standards and Technology) metering method as seminal work in this field. Our experiment results demonstrate that the running time of the proposed meter is around 60 s in a conventional desktop server, expecting better performance in high-end hardware, with an FHE scheme in HElib library where parameters support at least 80-bit security. We believe the proposed method can be further explored and used for a password metering in case that password secrecy is very important—the user’s password candidates should not be exposed to the meter and also an internal mechanism of password metering should not be disclosed to users and any other third parties.

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