A Social Network Simulation Game to Raise Awareness of Privacy Among School Children

In this paper, we address the problem of enhancing young people's awareness of the mechanisms involving privacy in online social networks by presenting an innovative approach based on gamification. In particular, we propose a web application that allows kids and teenagers to experience the typical dynamics of information spread through a realistic interactive simulation. Under the supervision of the teacher, the students are inserted in a small artificial social graph, and through the different stages of game, they can post sentences with different levels of sensitivity, and “like” or share messages published by friends. At the end of game session, the application calculate multiple behavioral scores that can be used by the teacher to raise the curiosity of the students and stimulate discussions. Moreover, a complete interactive report is generated to analyze every individual action of the terminated game sessions. Our educational tool has been employed within an extensive experimental study involving more than 450 kids and 22 teachers in seven Italian primary school institutes. The results show that our approach is stimulating and supports teachers in helping kids discover and recognize potential privacy risks in social network activities.

[1]  Evimaria Terzi,et al.  Reconstructing Randomized Social Networks , 2010, SDM.

[2]  Iván Martínez-Ortiz,et al.  Development of Game-Like Simulations for Procedural Knowledge in Healthcare Education , 2014, IEEE Transactions on Learning Technologies.

[3]  Guglielmo Trentin,et al.  Networked Collaborative Learning , 2010 .

[4]  Marc Prensky,et al.  Digital game-based learning , 2000, CIE.

[5]  Xiaowei Ying,et al.  On link privacy in randomizing social networks , 2010, Knowledge and Information Systems.

[6]  Luis de Marcos,et al.  Gamifying learning experiences: Practical implications and outcomes , 2013, Comput. Educ..

[7]  Anne Abt,et al.  Simulations And The Future Of Learning An Innovative And Perhaps Revolutionary Approach To E Learning , 2016 .

[8]  Ruggero G. Pensa,et al.  A privacy self-assessment framework for online social networks , 2017, Expert Syst. Appl..

[9]  Eden Litt,et al.  Understanding social network site users' privacy tool use , 2013, Comput. Hum. Behav..

[10]  Donald F. Towsley,et al.  Resisting structural re-identification in anonymized social networks , 2010, The VLDB Journal.

[11]  Pierre Dillenbourg,et al.  Collaborative Learning: Cognitive and Computational Approaches. Advances in Learning and Instruction Series. , 1999 .

[12]  Evimaria Terzi,et al.  A Framework for Computing the Privacy Scores of Users in Online Social Networks , 2009, ICDM.

[13]  Luis de Marcos,et al.  An empirical study comparing gamification and social networking on e-learning , 2014, Comput. Educ..

[14]  Jian Pei,et al.  The k-anonymity and l-diversity approaches for privacy preservation in social networks against neighborhood attacks , 2011, Knowledge and Information Systems.

[15]  Lei Zou,et al.  K-Automorphism: A General Framework For Privacy Preserving Network Publication , 2009, Proc. VLDB Endow..

[16]  C. Bonwell,et al.  Active learning : creating excitement in the classroom , 1991 .

[17]  David D. Jensen,et al.  Accurate Estimation of the Degree Distribution of Private Networks , 2009, 2009 Ninth IEEE International Conference on Data Mining.

[18]  Ruggero G. Pensa,et al.  Impact of Neighbors on the Privacy of Individuals in Online Social Networks , 2017, ICCS.

[19]  C.-H. Cheng,et al.  A mobile gamification learning system for improving the learning motivation and achievements , 2015, J. Comput. Assist. Learn..

[20]  Krishna P. Gummadi,et al.  Analyzing facebook privacy settings: user expectations vs. reality , 2011, IMC '11.

[21]  T. Graepel,et al.  Private traits and attributes are predictable from digital records of human behavior , 2013, Proceedings of the National Academy of Sciences.

[22]  K. Liu,et al.  Towards identity anonymization on graphs , 2008, SIGMOD Conference.

[23]  Jonghwa Park,et al.  The role of privacy fatigue in online privacy behavior , 2018, Comput. Hum. Behav..

[24]  Mark Newman,et al.  Networks: An Introduction , 2010 .

[25]  Balachander Krishnamurthy,et al.  Class-based graph anonymization for social network data , 2009, Proc. VLDB Endow..

[26]  Chris Clifton,et al.  A Guide to Differential Privacy Theory in Social Network Analysis , 2012, 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.

[27]  Y. Kafai.  Playing and Making Games for Learning , 2006, Games Cult..

[28]  Marco Furini,et al.  Location privacy and public metadata in social media platforms: attitudes, behaviors and opinions , 2014, Multimedia Tools and Applications.

[29]  Michael Netter,et al.  Friend Inspector: A Serious Game to Enhance Privacy Awareness in Social Networks , 2014, ArXiv.

[30]  M. Premkumar,et al.  Resolving Multi-party Privacy Conflicts in Social Media , 2018 .

[31]  Jerry Chih-Yuan Sun,et al.  An Interactive Game Approach for Improving Students’ Learning Performance in Multi-Touch Game-Based Learning , 2014, IEEE Transactions on Learning Technologies.

[32]  Krishna P. Gummadi,et al.  You are who you know: inferring user profiles in online social networks , 2010, WSDM '10.

[33]  Jose M. Such,et al.  How Socially Aware Are Social Media Privacy Controls? , 2016, Computer.