scholarly journals An improved Framework for Biometric Database’s privacy

2021 ◽  
Vol 13 (3) ◽  
Author(s):  
Ahmed EL-YAHYAOUI ◽  
Fouzia OMARY
Keyword(s):  
Homomorphic Encryption ◽  
Security And Privacy ◽  
Fully Homomorphic Encryption ◽  
Sensitive Data ◽  
Biometric Data ◽  
Java Programming ◽  
Biometric Systems ◽  
Database Privacy ◽  
Security Module ◽  
New Framework

Security and privacy are huge challenges in biometric systems. Biometrics are sensitive data that should be protected from any attacker and especially attackers targeting the confidentiality and integrity of biometric data. In this paper an extensive review of different physiological biometric techniques is provided. A comparative analysis of the various sus mentioned biometrics, including characteristics and properties is conducted. Qualitative and quantitative evaluation of the most relevant physiological biometrics is achieved. Furthermore, we propose a new framework for biometric database privacy. Our approach is based on the use of the promising fully homomorphic encryption technology. As a proof of concept, we establish an initial implementation of our security module using JAVA programming language.

scholarly journals A Privacy-Preserving Fully Homomorphic Encryption and Parallel Computation Based Biometric Data Matching

2020 ◽  
Author(s):  
Ferhat Ozgur Catak ◽  
Sule Yildirim Yayilgan ◽  
Mohamed Abomhara
Keyword(s):  
Privacy Preservation ◽  
Homomorphic Encryption ◽  
Practical Implementation ◽  
Fully Homomorphic Encryption ◽  
Biometric Data ◽  
General Data Protection Regulation ◽  
Data Matching ◽  
General Data ◽  
Encryption Method ◽  
Border Controls

One of the most reliable methods of authentication used today is biometric matching. This authentication process, which is done by using biometrics information such as fingerprint, iris, face, etc. is used in many application areas. Authentication at border gates is one of these areas. However, some restrictions have been introduced to storing and using such data, especially with the General Data Protection Regulation (GDPR). The main goal of this work is to find the practical implementation of fully homomorphic encryption-based biometric matching in border controls. In this paper, we propose a biometric authentication system based on hash expansion and fully homomorphic encryption features, considering these restrictions. One of the most significant drawbacks of the homomorphic encryption method is the long execution time. We solved this problem by executing the matching algorithm in parallel manner. The proposed scheme is implemented as proof-of-concept in the SMILE, and its advantages in privacy preservation has been demonstrated.

scholarly journals Oblivious Lookup-Tables

2016 ◽  
Vol 67 (1) ◽  
pp. 191-203
Author(s):  
Markus Stefan Wamser ◽  
Stefan Rass ◽  
Peter Schartner
Keyword(s):  
Medical Records ◽  
Homomorphic Encryption ◽  
Input Parameter ◽  
Software As A Service ◽  
Fully Homomorphic Encryption ◽  
Sensitive Data ◽  
New Approach ◽  
Encrypted Data ◽  
Practical Applications ◽  
Current State

Abstract Evaluating arbitrary functions on encrypted data is one of the holy grails of cryptography, with Fully Homomorphic Encryption (FHE) being probably the most prominent and powerful example. FHE, in its current state is, however, not efficient enough for practical applications. On the other hand, simple homomorphic and somewhat homomorphic approaches are not powerful enough to support arbitrary computations. We propose a new approach towards a practicable system for evaluating functions on encrypted data. Our approach allows to chain an arbitrary number of computations, which makes it more powerful than existing efficient schemes. As with basic FHE we do not encrypt or in any way hide the function, that is evaluated on the encrypted data. It is, however, sufficient that the function description is known only to the evaluator. This situation arises in practice for software as a Software as a Service (SaaS)-scenarios, where an evaluator provides a function only known to him and the user wants to protect his data. Another application might be the analysis of sensitive data, such as medical records. In this paper we restrict ourselves to functions with only one input parameter, which allow arbitrary transformations on encrypted data.

Applying Technical Standards to Biometric Systems

2016 ◽  
Vol 8 (2) ◽  
pp. 50-59
Author(s):  
Milorad Milinković ◽  
Miroslav Minović ◽  
Miloš Milovanović
Keyword(s):  
Data Security ◽  
Privacy Protection ◽  
Data Exchange ◽  
International Organization ◽  
The Other ◽  
Security And Privacy ◽  
Technical Standards ◽  
Biometric Data ◽  
Biometric Systems ◽  
Common Problems

Nowadays, the development and the application of biometric systems on one hand, and the large number of hardware and software manufacturers on the other, caused two the most common problems of biometric systems: a problem of interoperability between system's components as well as between different biometric systems and a problem of biometric data security and privacy protection, both in storage and exchange. Specifications and standards, such as BioAPI and CBEFF, registered and published as multiple standards by ISO (International Organization for Standardization), propose the establishment of single platform (BioAPI) to facilitate the functioning of the biometric systems regardless of hardware or software manufacturers, and unique format for data exchange (CBEFF) to secure biometric data. In this paper, these standards are analyzed in detail and considered as possible solutions to aforementioned problems.

scholarly journals A Verifiable Fully Homomorphic Encryption Scheme for Cloud Computing Security

Technologies ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 21
Author(s):  
Ahmed EL-YAHYAOUI ◽  
Mohamed Dafir ECH-CHERIF EL KETTANI
Keyword(s):  
Cloud Computing ◽  
Homomorphic Encryption ◽  
Encryption Scheme ◽  
Fully Homomorphic Encryption ◽  
Sensitive Data ◽  
Verifiable Computation ◽  
Multiplication Operation ◽  
Matrix Operations ◽  
Encryption Schemes ◽  
Simple Matrix

Performing smart computations in a context of cloud computing and big data is highly appreciated today. It allows customers to fully benefit from cloud computing capacities (such as processing or storage) without losing confidentiality of sensitive data. Fully homomorphic encryption (FHE) is a smart category of encryption schemes that enables working with the data in its encrypted form. It permits us to preserve confidentiality of our sensible data and to benefit from cloud computing capabilities. While FHE is combined with verifiable computation, it offers efficient procedures for outsourcing computations over encrypted data to a remote, but non-trusted, cloud server. The resulting scheme is called Verifiable Fully Homomorphic Encryption (VFHE). Currently, it has been demonstrated by many existing schemes that the theory is feasible but the efficiency needs to be dramatically improved in order to make it usable for real applications. One subtle difficulty is how to efficiently handle the noise. This paper aims to introduce an efficient and symmetric verifiable FHE based on a new mathematic structure that is noise free. In our encryption scheme, the noise is constant and does not depend on homomorphic evaluation of ciphertexts. The homomorphy of our scheme is obtained from simple matrix operations (addition and multiplication). The running time of the multiplication operation of our encryption scheme in a cloud environment has an order of a few milliseconds.

scholarly journals Compare encryption performance across devices to ensure the security of the IOT

2020 ◽  
Vol 20 (2) ◽  
pp. 894
Author(s):  
A.YU. Pyrkova ◽  
ZH.E. Temirbekova
Keyword(s):  
Homomorphic Encryption ◽  
Security And Privacy ◽  
Symmetric Cryptography ◽  
Fully Homomorphic Encryption ◽  
Cryptographic Primitives ◽  
Development Platform ◽  
Encryption And Decryption ◽  
Detailed Assessment ◽  
Iot Devices ◽  
The Internet Of Things

The Internet of Things (IoT) combines many devices with various platforms, computing capabilities and functions. The heterogeneity of the network and the ubiquity of IoT devices place increased demands on security and privacy protection. Therefore, cryptographic mechanisms must be strong enough to meet these increased requirements, but at the same time they must be effective enough to be implemented on devices with disabilities. One of the limited devices are microcontrollers and smart cards. This paper presents the performance and memory limitations of modern cryptographic primitives and schemes on various types of devices that can be used in IoT. In this article, we provide a detailed assessment of the performance of the most commonly used cryptographic algorithms on devices with disabilities that often appear on IoT networks. We relied on the most popular open source microcontroller development platform, on the mbed platform. To provide a data protection function, we use cryptography asymmetric fully homomorphic encryption in the binary ring and symmetric cryptography AES 128 bit. In addition, we compared run-time encryption and decryption on a personal computer (PC) with Windows 7, the Bluetooth Low Energy (BLE) Nano Kit microcontroller, the BLE Nano 1.5, and the smartcard ML3-36k-R1.

scholarly journals Homomorphic Encryption Using Enhanced BGV Encryption Scheme For Cloud Security

2018 ◽  
Vol 7 (03) ◽  
pp. 23785-23789
Author(s):  
S.V.Suriya Prasad ◽  
K. Kumanan
Keyword(s):  
Homomorphic Encryption ◽  
Cloud Service ◽  
Cloud Security ◽  
Encryption Scheme ◽  
Public Cloud ◽  
Fully Homomorphic Encryption ◽  
Sensitive Data ◽  
Encrypted Data ◽  
Or Applications

Fully Homomorphic Encryption is used to enhance the security incase of un-trusted systems or applications that deals with sensitive data. Homomorphic encryption enables computation on encrypted data without decryption. Homomorphic encryption prevents sharing of data within the cloud service where data is stored in a public cloud . In Partially Homomorphic Encryption it performs either additive or multiplicative operation, but not both operation can be carried out at a same time. Whereas , in case of Fully Homomorphic Encryption both operations can be carried out at same time. In this model , Enhanced BGV Encryption Technique is used to perform FHE operations on encrypted data and sorting is performed using the encrypted data

scholarly journals t-BMPNet: Trainable Bitwise Multilayer Perceptron Neural Network over Fully Homomorphic Encryption Scheme

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Joon Soo Yoo ◽  
Ji Won Yoon
Keyword(s):  
Neural Network ◽  
Homomorphic Encryption ◽  
Activation Function ◽  
Significant Error ◽  
Security And Privacy ◽  
Encryption Scheme ◽  
Fully Homomorphic Encryption ◽  
Encrypted Data ◽  
Lattice Problem ◽  
High Level

Homomorphic encryption (HE) is notable for enabling computation on encrypted data as well as guaranteeing high-level security based on the hardness of the lattice problem. In this sense, the advantage of HE has facilitated research that can perform data analysis in an encrypted state as a purpose of achieving security and privacy for both clients and the cloud. However, much of the literature is centered around building a network that only provides an encrypted prediction result rather than constructing a system that can learn from the encrypted data to provide more accurate answers for the clients. Moreover, their research uses simple polynomial approximations to design an activation function causing a possibly significant error in prediction results. Conversely, our approach is more fundamental; we present t-BMPNet which is a neural network over fully homomorphic encryption scheme that is built upon primitive gates and fundamental bitwise homomorphic operations. Thus, our model can tackle the nonlinearity problem of approximating the activation function in a more sophisticated way. Moreover, we show that our t-BMPNet can perform training—backpropagation and feedforward algorithms—in the encrypted domain, unlike other literature. Last, we apply our approach to a small dataset to demonstrate the feasibility of our model.

scholarly journals Modified Fully Homomorphic Encryption based on Parallel Processing in Cloud Computing

2021 ◽  
pp. 250-262
Author(s):  
Parth Tandel ◽  
Abhinav Shubhrant ◽  
Mayank Sohani
Keyword(s):  
Cloud Computing ◽  
Parallel Processing ◽  
Data Security ◽  
Privacy Protection ◽  
Homomorphic Encryption ◽  
Security And Privacy ◽  
Fully Homomorphic Encryption ◽  
Privacy And Security ◽  
Encrypted Data ◽  
Encryption Algorithms

Cloud Computing is widely regarded as the most radically altering trend in information technology. However, great benefits come with great challenges, especially in the area of data security and privacy protection. Since standard cloud computing uses plaintext, certain encryption algorithms were implemented in the cloud for security reasons, and ‘encrypted' data was then stored in the cloud. Homomorphic Encryption (HE), a modern kind of encryption strategy, is born as a result of this change. Primarily, the paper will focus on implementing a successful Homomorphic Encryption (HE) scheme for polynomials. Furthermore, the objective of the paper is to propose, produce and implement a method to convert the already implemented sequentially processing Homomorphic Encryption into parallel processing Homomorphic Encryption (HE) using a Parallel Processing concept (Partitioning, Assigning, Scheduling, etc) and thereby producing a better performing Homomorphic Encryption (HE) called Fully Homomorphic Encryption (FHE). Fully Homomorphic Encryption (FHE) is an encryption technique that can perform specific analytical operations, functions and methods on normal or encrypted data and can still perform traditional encryption results as performed on plaintext. The three major reasons for implementing Fully Homomorphic Encryption (FHE) are advantages like no involvement of third parties, trade-off elimination between privacy and security and quantum safety.

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