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In this section, we provide a summary of the literature by comparing the privacy preservation techniques and the preserving data in social network as shown in Table 1.
In general, some privacy preservation techniques are developed for preserving specific information but are not applied to other information.
A number of privacy preservation techniques exist some of which focus on preserving the privacy of outsourced data i.e. for the secure data storage and computation at third party [1, 3, 4].
The proposed privacy preservation techniques utilise multiple IoT cloud data stores to protect the privacy of data collected from IoT.
In this paper, we investigate the privacy preservation techniques for social network integration.
The choice of the privacy preservation techniques also depends on the application of social network analysis.
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We have also analyzed our scheme on real life data and found that the privacy preservation technique does not affect the utility (performance) of the basic biometric system.
This research proposes a multi-agent architecture coupled with privacy preserving techniques to facilitate data sharing while preserving privacy.
"Privacy Preserving Methods in Big Data" section covers the privacy preserving techniques using big data.
In particular, we propose innovative techniques for privacy preservation of IoT data, introduce a privacy preserving IoT Architecture, and also describe the implementation of an efficient proof of concept system that utilises all these to ensure that IoT data remains private.
First of all, the techniques for privacy preservation strongly depend on the nature of the data to be protected.
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