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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.
It also discusses confidentiality (privacy preservation) and presents a new algorithm for privacy-preserving density-based clustering.
This ensures privacy preservation with low computational cost.
We conclude that the proposed protocol guarantees identity privacy preservation.
Moreover, the presented scheme satisfies good privacy preservation.
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Privacy-preservation of bidders' bids.
Figure 13 illustrates privacy-preservation under algorithmic summarization levels in Nervousnet.
Figure 12 illustrates privacy-preservation under fixed summarization levels in the Nervousnet project.
Figure 13 Privacy-preservation under algorithmic summarization levels in the Nervousnet project.
Figure 11 illustrates privacy-preservation under algorithmic summarization levels in the ECBT project.
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