國立虎尾科技大學 |

Improving Social Network Inference Attacks via Deep Neural Networks.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Improving Social Network Inference Attacks via Deep Neural Networks./
作者:	Mei, Bo.
面頁冊數:	1 online resource (52 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.
Contained By:	Dissertation Abstracts International79-09B(E).
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780355873276

Improving Social Network Inference Attacks via Deep Neural Networks.
Mei, Bo.

Improving Social Network Inference Attacks via Deep Neural Networks. - 1 online resource (52 pages)

Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.

Thesis (Ph.D.)--The George Washington University, 2018.

Includes bibliographical references

In modern society, social networks play an important role for online users. However, one unignorable problem behind the booming of the services is privacy issues. At the same time, neural networks have been swiftly developed in recent years, and are proven to be very effective in inference attacks.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355873276Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Improving Social Network Inference Attacks via Deep Neural Networks.
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245 1 0 $a Improving Social Network Inference Attacks via Deep Neural Networks.
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300 $a 1 online resource (52 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.
500 $a Adviser: Xiuzhen Cheng.
502 $a Thesis (Ph.D.)--The George Washington University, 2018.
504 $a Includes bibliographical references
520 $a In modern society, social networks play an important role for online users. However, one unignorable problem behind the booming of the services is privacy issues. At the same time, neural networks have been swiftly developed in recent years, and are proven to be very effective in inference attacks.
520 $a In the first part of this dissertation, a new framework for inference attacks in social networks is developed, which smartly integrates and modifies the existing state-of-the-art Convolutional Neural Network (CNN) models. As a result, the framework can fit wider applicable scenarios for inference attacks no matter whether a user has a legit profile image or not. Moreover, the framework is able to boost the existing high-accuracy CNN for sensitive information prediction. In addition to the framework, the dissertation also shows the detailed configuration of a Fully Connected Neural Network (FCNN) for inference attacks. Furthermore, traditional machine learning algorithms are implemented to compare the results from the constructed FCNN.
520 $a In the second part of the dissertation, a scheme for social network de-anonymization via Recurrent Neural Network (RNN) is proposed. This scheme improves both traditional structure-based schemes, and traditional Natural Language Processing (NLP) schemes for social network de-anonymization. Specifically, the new scheme is friendlier to modern social network data than structure-based schemes. It also provides less data preprocessing time, easier implementation, and higher tolerance to noisy data than traditional NLP schemes. The results show that the new scheme inhibits many drawbacks of the existing methods, and increases de-anonymization rate.
520 $a Overall, the key contribution of the dissertation lies in the integration and modification of existing deep neural networks for social network inference attacks. The major challenge is how to adapt deep learning techniques onto modern social networks to maximize both inference attack rate and de-anonymization rate.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a The George Washington University. $b Computer Science. $3 1148676
773 0 $t Dissertation Abstracts International $g 79-09B(E).
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