A distributed randomization framework for privacy preservation in big data

The privacy preservation is a big challenge for data generated from various sources such as social networking sites, online transaction, weather forecast to name a few. Due to the socialization of the internet and cloud computing pica bytes of unstructured data is generated online with intrinsic val...

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Main Authors: Shukla, Samiksha, Sadashivappa, G.
Format: Conference Proceeding
Language:English
Subjects:
Anonymization
Computer architecture
Confidentiality
Encryption
Handheld computers
Packetization
Performance analysis
Privacy
Protocols
Secure Multi-Party Computation (SMC)
Security
Trusted third party (TTP)
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Sadashivappa, G.
description The privacy preservation is a big challenge for data generated from various sources such as social networking sites, online transaction, weather forecast to name a few. Due to the socialization of the internet and cloud computing pica bytes of unstructured data is generated online with intrinsic values. The inflow of big data and the requirement to move this information throughout an organization has become a new target for hackers. This data is subject to privacy laws and should be protected. The proposed protocol is one step toward the security in case of above circumstances where data is coming from multiple participants and all are concerned about individual privacy and confidentiality.
doi_str_mv 10.1109/CSIBIG.2014.7056940
format conference_proceeding
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source IEEE Electronic Library (IEL) Conference Proceedings
subjects Anonymization
Computer architecture
Confidentiality
Encryption
Handheld computers
Packetization
Performance analysis
Privacy
Protocols
Secure Multi-Party Computation (SMC)
Security
Trusted third party (TTP)
title A distributed randomization framework for privacy preservation in big data
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