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Matching user identities across social networks with limited profile data

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Abstract

Privacy preservation is a primary concern in social networks which employ a variety of privacy preservations mechanisms to preserve and protect sensitive user information including age, location, education, interests, and others. The task of matching user identities across different social networks is considered a challenging task. In this work, we propose an algorithm to reveal user identities as a set of linked accounts from different social networks using limited user profile data, i.e., user-name and friendship. Thus, we propose a framework, ExpandUIL, that includes three standalone algorithms based on (i) the percolation graph matching in ExpandFullName algorithm, (ii) a supervised machine learning algorithm that works with the graph embedding, and (iii) a combination of the two, ExpandUserLinkage algorithm. The proposed framework as a set of algorithms is significant as, (i) it is based on the network topology and requires only name feature of the nodes, (ii) it requires a considerably low initial seed, as low as one initial seed suffices, (iii) it is iterative and scalable with applicability to online incoming stream graphs, and (iv) it has an experimental proof of stability over a real ground-truth dataset. Experiments on real datasets, Instagram and VK social networks, show upto 75% recall for linked accounts with 96% accuracy using only one given seed pair.

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Acknowledgements

We are especially grateful to Sadegh Nobari for his fruitful comments and inspiration, and also to the anonymous FCS reviewers for their constructive feedback and helpful discussions.

Author information

Authors and Affiliations

  1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China

    Ildar Nurgaliev, Qiang Qu, Seyed Mojtaba Hosseini Bamakan & Muhammad Muzammal

  2. Sberbank, Moscow, 121170, Russia

    Ildar Nurgaliev

  3. Department of Management, Yazd University, Yazd, 89195-741, Iran

    Seyed Mojtaba Hosseini Bamakan

  4. Department of Computer Science, Bahria University, Islamabad, 44000, Pakistan

    Muhammad Muzammal

Authors
  1. Ildar Nurgaliev
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  2. Qiang Qu
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  3. Seyed Mojtaba Hosseini Bamakan
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  4. Muhammad Muzammal
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Corresponding author

Correspondence to Qiang Qu.

Additional information

Ildar Nurgaliev received the MSc degree in Data Science from the Innopolis University, Tatarstan, Russia in 2017. Currently, he is in R&D at Sberbank in the field of Natural Language Understanding and Knowledge graphs. Previously he was a research engineer at Huawei Moscow research center and Ozon.ru. He was a student at CERN OpenLab in 2016, Geneva, Switzerland. His current research interests include natural language understanding, image enhancement, and blockchain.

Qiang Qu is an associate professor at Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences (CAS), and the director of Guangdong Provincial R&D Center of Blockchain and Distributed IoT Security, China. He is a candidate for the CAS Pioneer Hundred Talents Program. He received an MSc degree in computer science from Peking University, China and a PhD degree from Aarhus University, Denmark. His current research interests are in dataintensive applications and systems, focusing on efficient and scalable algorithm design, blockchain, data sense-making, and mobility intelligence. His recent research has been published in leading journals and international conferences, including ACM SIGMOD, VLDB, AAAI, the IEEE transactions on Data Engineering, the IEEE Transactions on Intelligent Transportation Systems, and Information Sciences. He was a TPC member of several prestige conferences, and he chaired workshops in VLDB 2018, VLDB 2017, ICDM 2015, and APWEB-WAIM 2017 on mobility analysis.

Seyed Mojtaba Hosseini Bamakan is an assistant professor at the Department of Management, Yazd University, Iran, a postdoctoral researcher at Shenzhen Institute of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), China. He received his PhD degree in Data Science from the University of Chinese Academy of Sciences (UCAS), China in 2017, and his master’s degree in IT management field from Allameh Tabataba’i University (ATU), Iran in 2009. His current research interests include business intelligence, data mining, and intelligent optimization techniques.

Muhammad Muzammal is an associate professor at the Department of Computer Science, Bahria University, Pakistan, a visiting associate professor under CAS President’s International Fellowship Initiative (PIFI) at the Centre of Big Mobile Intelligence, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, and a Vice Director of Guangdong Provincial R&D Centre of Blockchain and Distributed IoT Security, Guangdong, China. He received a PhD degree from the University of Leicester, UK in 2012. Before, that he was a software analyst at LMKR. He received the master’s and bachelor’s degrees from FAST-NU, Pakistan, and IIUI, Pakistan, in 2007 and 2005, respectively. His research interests are in large scale data mining including algorithm design and mobility data mining. Recently, he is interested in blockchain technology with a focus on decentralized systems and mining.

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Nurgaliev, I., Qu, Q., Bamakan, S.M.H. et al. Matching user identities across social networks with limited profile data. Front. Comput. Sci. 14, 146809 (2020). https://doi.org/10.1007/s11704-019-8235-9

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