國立虎尾科技大學 |

Privacy Preserving Friend Discovery in Mobile Social Networks.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Privacy Preserving Friend Discovery in Mobile Social Networks./
作者:	Li, Hongjuan.
面頁冊數:	1 online resource (88 pages)
附註:	Source: Dissertation Abstracts International, Volume: 77-06(E), Section: B.
Contained By:	Dissertation Abstracts International77-06B(E).
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9781339386805

Privacy Preserving Friend Discovery in Mobile Social Networks.
Li, Hongjuan.

Privacy Preserving Friend Discovery in Mobile Social Networks. - 1 online resource (88 pages)

Source: Dissertation Abstracts International, Volume: 77-06(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

Mobile social networking has been increasingly popular with the explosive growth of mobile devices. By allowing mobile users to interact with potential friends around the real world, it enables new social interactions as a complement to web-based online social networks. Motived by this feature, many exciting applications have been developed, yet the challenge of privacy protection is also aroused. This dissertation studies the problem of privacy preserving in mobile social networks. We propose different mechanisms for various privacy requirements.

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

Mode of access: World Wide Web

ISBN: 9781339386805Subjects--Topical Terms:

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

554714
Electronic books.

Privacy Preserving Friend Discovery in Mobile Social Networks.
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500 $a Source: Dissertation Abstracts International, Volume: 77-06(E), Section: B.
500 $a Adviser: Xiuzhen Cheng.
502 $a Thesis (Ph.D.) $c The George Washington University $d 2016.
504 $a Includes bibliographical references
520 $a Mobile social networking has been increasingly popular with the explosive growth of mobile devices. By allowing mobile users to interact with potential friends around the real world, it enables new social interactions as a complement to web-based online social networks. Motived by this feature, many exciting applications have been developed, yet the challenge of privacy protection is also aroused. This dissertation studies the problem of privacy preserving in mobile social networks. We propose different mechanisms for various privacy requirements.
520 $a The first algorithm we proposed is a secure friend discovery mechanism based on encounter history in mobile social networks, which mainly focuses on the location privacy. By exploring the fact that sharing encounters indicate common activities and interests, our scheme can help people make friends with likeminded strangers nearby. We provide peer-to-peer confidential communications with the location privacy and encounter privacy being strictly preserved. Unlike most existing works that either rely on a trusted centralized server or existing social relationships, our algorithm is designed in an ad-hoc model with no such limitation. As a result, our design is more suitable and more general for mobile social scenarios.
520 $a We also develop an efficient customized privacy preserving mechanism, which not only protects the privacy of users' profile, but also establishes a verifiable secure communication channel between matching users. Besides, the initiator has the freedom to set a customized request profile by choosing the interested attributes and giving each attribute a specific value. Moreover, the request profile's privacy protection level is customized by the initiator according to his/her own privacy requirements. We also consider the collusion attacks among unmatched users. To the best of our knowledge, this is the first work to address such security threat.
520 $a Our protocol guarantees only exactly matching users are able to communicate with the initiator securely, while as little as possible information can be obtained by other participants. To increase the matching efficiency, our design adopts the Bloom filter to efficiently exclude most unmatched users. As a result, our design effectively protects the profile privacy and efficiently decreases the computation overhead. Our third work for this dissertation explores fine-grained profile matching by associating a user-specific numerical value with each attribute to indicate the level of interest. And the profile similarity is computed with a secure dot-product. While existing studies are mainly focused on leveraging rich cryptography algorithms to prevent privacy leakage, we consider a novel cooperative framework by mixing some random noise with the private data to preserve privacy. By carefully tuning the amount of information owned by each party, we guarantee that the privacy is effectively preserved while the matching result of two profiles can be cooperatively obtained. After giving an introduction of the basic mechanism, we detail two enhanced mechanisms by taking collusion attack and verifiability into consideration. With no expensive encryption algorithms involved, our methods are more practical for real-world applications.
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 77-06B(E).
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