Privacy-preserving profile matching using the social graph

Jeckmans, Arjan; Tang, Qiang; Hartel, Pieter H.

doi:10.1109/cason.2011.6085916

Cited by 3 publications

(4 citation statements)

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“…The goal is to query or mine graph data while simultaneously maintaining the privacy of the property and/or structure data of the graph. Privacy-preserving graph analysis has been used to study many problems, i.e., subgraph query [40] , vertex reachability [41] , inference attacks [42] , shortest path [43] , spanning tree [44] , and graph matching [45] . In this paper, for the first time, we consider the problem of privacypreserving graph clustering with both relationship and location information.…”

Section: Related Workmentioning

confidence: 99%

Location- and relation-based clustering on privacy-preserving social networks

Yin

Shen

2018

Tinshhua Sci. Technol.

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Graph clustering has a long-standing problem in that it is difficult to identify all the groups of vertices that are cohesively connected along their internal edges but only sparsely connected along their external edges. Apart from structural information in social networks, the quality of the location-information clustering has been improved by identifying clusters in the graph that are closely connected and spatially compact. However, in real-world scenarios, the location information of some users may be unavailable for privacy reasons, which renders existing solutions ineffective. In this paper, we investigate the clustering problem of privacy-preserving social networks, and propose an algorithm that uses a prediction-and-clustering approach. First, the location of each invisible user is predicted with a probability distribution. Then, each user is iteratively assigned to different clusters. The experimental results verify the effectiveness and efficiency of our method, and our proposed algorithm exhibits high scalability on large social networks.

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Section: Related Workmentioning

confidence: 99%

Location- and relation-based clustering on privacy-preserving social networks

Yin

Shen

2018

Tinshhua Sci. Technol.

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“…Secondly, we propose a solution for protecting users' private profile attributes in OSNs. In the solution, a transitive and uni-directional proxy reencryption scheme [5] allows users to encrypt their private profile attributes with their own public keys. Based on the unilateral set intersection cardinality protocol, we design an Online-Offline profile matching protocol, which allows two users to compute their profile similarity and one of them can stay offline.…”

Section: Our Contributionmentioning

confidence: 99%

“…The proposed solution employs transitive and uni-directional proxy re-encryption cryptosystem , namely (KeyGen, Enc, Dec, Pextract, Preenc) [5], and the following two new protocols.…”

Section: New Cryptographic Building Blocksmentioning

confidence: 99%

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Towards a Privacy-Preserving Solution for OSNs

Tang

2013

Network and System Security

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Abstract. In this short paper, we describe a solution to protect users' privacy in online social networks (OSNs). The solution achieves the following functionalities: (1) it enables users to store their private data securely; (2) it enables users, from the same or different OSNs, to compute their similarity through a secure protocol; (3) it enables similar users to establish a session key for secure communication. Different from existing solutions in the literature, which often rely on a global public key infrastructure or/and traditional key distribution techniques, the proposed solution leverages on the trust between friends and the entropy of users' private attributes.

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Sistem Pendukung Keputusan Produk Patiseri Terbaik dengan Metode Profile Matching

Laya

Zainnur

2015

JURNAL MULTIMEDIA NETWORKING INFORMATICS

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Perkembangan teknologi yang melesat saat kini menghadirkan satu kemudahan dalam kehidupan manusia. Untuk memudahkan dan meningkatkan sistem produksi terutama pada industri rumahan patiseri, maka diperlukan sebuah sistem pendukung keputusan yang dapat membantu pihak industri rumahan tersebut dalam memutuskan jumlah produksi berdasarkan jumlah produk yang laku, terbaik, dan terpopuler di masyarakat. Dengan menggunakan sebuah kuesioner untuk mengetahui produk terbaik dan terpopuler di masyarakat, serta menggunakan metode profile matching untuk membantu proses penghitungan seluruh data jawaban kuesioner sehingga menghasilkan peringkat dari produk-produk yang telah dipilih oleh pelanggan melaluiproses pengisian kuesioner. Profile Matching adalah sebuah mekanisme pengambilan keputusan dengan mengasumsikan bahwa terdapat tingkat variabel prediktor yang ideal yang harus dipenuhi oleh subyek yang diteliti, bukannya tingkat minimal yang harus dipenuhi atau dilewati. Hasil yang dicapai dalam perhitungan menggunakan metode profile matching ini adalah sebuah nilai yang akan diurutkan dari yang terbesar hingga terkecil dan akan menghasilkan sebuah urutan peringkat. Hasil perbandingan perhitungan secara manual dan otomatis melalui aplikasi didapat hasil yang sama sehingga dapat mempercepat proses apabila digunakan aplikasi yang telah dibuat.

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Privacy-preserving profile matching using the social graph

Cited by 3 publications

References 10 publications

Location- and relation-based clustering on privacy-preserving social networks

Location- and relation-based clustering on privacy-preserving social networks

Towards a Privacy-Preserving Solution for OSNs

Sistem Pendukung Keputusan Produk Patiseri Terbaik dengan Metode Profile Matching

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