scholarly journals Parallel Secure Computation Scheme for Biometric Security and Privacy in Standard-Based BioAPI Framework

10.5772/15559 ◽  
2011 ◽  
Author(s):  
Arun P. Kumara Krishan ◽  
Bon K. ◽  
Adam Ramirez
Keyword(s):  
Secure Computation ◽  
Security And Privacy ◽  
Biometric Security ◽  
Computation Scheme

scholarly journals A Survey on Secure Computation Based on Homomorphic Encryption in Vehicular Ad Hoc Networks

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4253
Author(s):  
Xiaoqiang Sun ◽  
F. Richard Yu ◽  
Peng Zhang ◽  
Weixin Xie ◽  
Xiang Peng
Keyword(s):  
Ad Hoc Networks ◽  
Cyber Security ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Homomorphic Encryption ◽  
Secure Computation ◽  
Security And Privacy ◽  
Future Research ◽  
Vehicle Data ◽  
Hoc Networks

In vehicular ad hoc networks (VANETs), the security and privacy of vehicle data are core issues. In order to analyze vehicle data, they need to be computed. Encryption is a common method to guarantee the security of vehicle data in the process of data dissemination and computation. However, encrypted vehicle data cannot be analyzed easily and flexibly. Because homomorphic encryption supports computations of the ciphertext, it can completely solve this problem. In this paper, we provide a comprehensive survey of secure computation based on homomorphic encryption in VANETs. We first describe the related definitions and the current state of homomorphic encryption. Next, we present the framework, communication domains, wireless access technologies and cyber-security issues of VANETs. Then, we describe the state of the art of secure basic operations, data aggregation, data query and other data computation in VANETs. Finally, several challenges and open issues are discussed for future research.

Enhanced Biometric Security and Privacy Using ECG on the Zynq SoC

2016 ◽  
pp. 179-202
Author(s):  
Amine Ait Si Ali ◽  
Xiaojun Zhai ◽  
Abbes Amira ◽  
Faycal Bensaali ◽  
Naeem Ramzan
Keyword(s):  
Security And Privacy ◽  
Biometric Security

scholarly journals Privacy-Aware MapReduce Based Multi-Party Secure Skyline Computation

Information ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 207
Author(s):  
Saleh Ahmed ◽  
Mahboob Qaosar ◽  
Asif Zaman ◽  
Md. Anisuzzaman Siddique ◽  
Chen Li ◽  
...  
Keyword(s):  
Data Privacy ◽  
Large Scale ◽  
Communication Complexity ◽  
Cost Effective ◽  
Privacy Preserving ◽  
Secure Computation ◽  
Security And Privacy ◽  
Computation Method ◽  
Computation Model ◽  
Privacy And Security

Selecting representative objects from a large-scale dataset is an important task for understanding the dataset. Skyline is a popular technique for selecting representative objects from a large dataset. It is obvious that the skyline computation from the collective databases of multiple organizations is more effective than the skyline computed from a database of a single organization. However, due to privacy-awareness, every organization is also concerned about the security and privacy of their data. In this regards, we propose an efficient multi-party secure skyline computation method that computes the skyline on encrypted data and preserves the confidentiality of each party’s database objects. Although several distributed skyline computing methods have been proposed, very few of them consider the data privacy and security issues. However, privacy-preserving multi-party skyline computing techniques are not efficient enough. In our proposed method, we present a secure computation model that is more efficient in comparison with existing privacy-preserving multi-party skyline computation models in terms of computation and communication complexity. In our computation model, we also introduce MapReduce as a distributive, scalable, open-source, cost-effective, and reliable framework to handle multi-party data efficiently.

Secure Multiparty Computing Protocol

2013 ◽  
pp. 132-143
Author(s):  
Zulfa Shaikh ◽  
Poonam Garg
Keyword(s):  
Secure Computation ◽  
Secure Multiparty Computation ◽  
Security And Privacy ◽  
Multiparty Computation ◽  
Sensitive Data ◽  
Number Of Parties

Secure Multiparty Computation (SMC) can be defined as n number of parties who do joint computation on their inputs (x1, x2…xn) using some function F and want output in the form of y. The increase in sensitive data on a network raises concern about the security and privacy of inputs. During joint computation, each party wants to preserve the privacy of their inputs. Therefore, there is a need to define an efficient protocol that maintains privacy, security, and correctness parameters of SMC. In this chapter, an approach towards secure computation is provided and analyzed with security graphs.

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