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An Overview of Transfer Learning Focused on Asymmetric Heterogeneous Approaches Read chapter Read first chapter

2018 | OriginalPaper | Chapter

The Case for Personalized Anonymization of Database Query Results

Authors : Axel Michel, Benjamin Nguyen, Philippe Pucheral

Published in: Data Management Technologies and Applications

Publisher: Springer International Publishing

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Abstract

The benefit of performing Big data computations over individual’s microdata is manifold, in the medical, energy or transportation fields to cite only a few, and this interest is growing with the emergence of smart disclosure initiatives around the world. However, these computations often expose microdata to privacy leakages, explaining the reluctance of individuals to participate in studies despite the privacy guarantees promised by statistical institutes.
In this paper, we consolidate our previous results to show how it is possible to push personalized privacy guarantees in the processing of database queries. By doing so, individuals can disclose different amounts of information (i.e. data at different levels of accuracy) depending on their own perception of the risk, and we discuss the different possible semantics of such models.
Moreover, we propose a decentralized computing infrastructure based on secure hardware enforcing these personalized privacy guarantees all along the query execution process. A complete performance analysis and implementation of our solution show the effectiveness of the approach to tackle generic large scale database queries.

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previous chapter ChronoGraph: A Versioned TinkerPop Graph Database
Footnotes
1
We thus do not consider applications such as targeted advertising, who seek to characterize the users at an individual level.
 
5
Yahoo ‘state’ hackers stole data from 500 million users - BBC News. www.​bbc.​co.​uk/​news/​world-us-canada-37447016.
 
8
Since this clause is an holistic function, we can compute it while the aggregation phase by adding naively each distinct value under a list or using a cardinality estimation algorithm such as HyperLogLog [18].
 
9
Each TDS can contribute to any phase of the protocol, depending on its availability, independently of the fact that it participated to the collection phase.
 
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Metadata
Title
The Case for Personalized Anonymization of Database Query Results
Authors
Axel Michel
Benjamin Nguyen
Philippe Pucheral
Copyright Year
2018
Book
Data Management Technologies and Applications

Print ISBN: 978-3-319-94808-9

Electronic ISBN: 978-3-319-94809-6

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-94809-6_13

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