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Researching Data Privacy
Models in eLearning
Malinka Ivanova, Technical University of Sofia
Gabriela Grosseck, West Univer...
Aim
To develop a model of students’ data privacy
supporting educators in educational settings
and not harming the students...
Introduction
“e” world
Business
Finance
Health
Government
Justice
Society
Learning
collection of a
vast amount of
private ...
Introduction
Registration
personal data:
full name
address
phone number
gender
ages
email address
Profile
Background
Prefe...
Questions
• What kind of privacy data is enough for educators to manage a
successful learning process?
• What kind of data...
Privacy aspects in eLearning
Privacy aspects in
eLearning
Data privacy to
protect learners
Data privacy to improve
learning
Privacy in health care and medicine
• Electronic health records - information is used for:
• diagnosis and treatment
• for...
Privacy in health care and medicine
• Security of data privacy could be achieved after applying a set of
actions:
• inform...
Privacy in Ecommerce
• Ecommerce activities:
• Administration
• Legislation for intellectual property protection
• Confide...
Privacy in Ecommerce
• From the side of selling and business companies, personal data can be protected
through combination...
Privacy in eGovernment
• Steps for securing eGovernment systems:
• development of policy strategy
• realization of secure ...
Privacy
• There are a wide variety of models proposing solutions for protection
of private data and combining components o...
Measures to
protect users’
privacy
Data privacy
Economic
measures
Social measures
Legal measures
Institutional
measures
Te...
Privacy in eLearning
Privacy aspects
in eLearning
Data privacy
to protect
learners
Data privacy to
improve
learning
Learne...
Privacy in eLearning
• Privacy risks
• use of unsecure TCP/IP protocols
• use of higher level of protocols like HTTP, SMTP...
Privacy in eLearning
• The violations:
• unauthorized access to data, stored in records with learner history
• unappropria...
Privacy in eLearning
• Solutions
• eLearning environment to allow:
• pseudonymity
• anonymity
• information sharing based ...
Privacy in eLearning
• Collaborative and privacy-aware eLearning platform BluES is specially
developed to recognize the pr...
Privacy for learning improvement
• All examples found in scientific papers - treat the role of anonymity to
stimulate and ...
Survey and Results
• The survey is designed with two goals:
• to understand the students’ opinion whether their privacy is...
Survey and Results
• The questions are grouped in four categories:
(1) use of educational software (including social netwo...
Survey and Results
Students’ predisposition to share private data in educational software
Survey and Results
Students’ predisposition to share educational background
Survey and Results
Students’ predisposition to give information about learning preferences
Survey and Results
Students’ answers about whether intelligent technologies could support their learning
Survey and Results
Students’ answers about whether intelligent technologies violate their privacy space
Survey and Results
Students’ opinion about whether they have to possess an opportunity to decide what kind of
personal dat...
Survey and Results
Students’ answers about the features of educational software allowing them to choose what
kind of priva...
Data Privacy Model in
eLearning
Privacy in eLearning
Student side
-what information
share
-what relationships
make
-how hi...
Conclusion
• Data privacy should be reached after applying a suitable combination of
measures
• The main components in the...
• Thank you for your attention!
Thanks for pictures:
http://healthworkscollective.com/keithtullyy/309681/how-technology-af...
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Researching Data Privacy in eLearning

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communication presented at ITHET 2015, IEETeL2015, 11-13 June, 2015, Caparica, Lisbon, Portugal by Malinka Ivanova (http://www.slideshare.net/malinkaiv)

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Researching Data Privacy in eLearning

  1. 1. Researching Data Privacy Models in eLearning Malinka Ivanova, Technical University of Sofia Gabriela Grosseck, West University of Timisoara Carmen Holotescu, University Politehnica Timisoara ITHET 2015, IEETeL2015, 11-13 June, 2015, Caparica, Lisbon, Portugal
  2. 2. Aim To develop a model of students’ data privacy supporting educators in educational settings and not harming the students’ privacy interests
  3. 3. Introduction “e” world Business Finance Health Government Justice Society Learning collection of a vast amount of private data
  4. 4. Introduction Registration personal data: full name address phone number gender ages email address Profile Background Preferences learning progress Tracking mechanisms Adaptive technologies Assistive technologies Social networking in education Learning analytics eLearning
  5. 5. Questions • What kind of privacy data is enough for educators to manage a successful learning process? • What kind of data the students are predisposed to share in order to successfully accomplish their learning activities?
  6. 6. Privacy aspects in eLearning Privacy aspects in eLearning Data privacy to protect learners Data privacy to improve learning
  7. 7. Privacy in health care and medicine • Electronic health records - information is used for: • diagnosis and treatment • for improvement the existing healthcare system • for development of healthcare policy • for research in medical science • This information is available for insurance companies, payment offices in medical organizations, for the purposes of several government initiatives, for statistical institutions • The risks for patients: • from an internal agent who does not possess such privileges or from an external agent who steals information from the information system • from an internal agent with privileges to process such data (Appari, Johnson, 2010)
  8. 8. Privacy in health care and medicine • Security of data privacy could be achieved after applying a set of actions: • information systems security • public policy • intra- and inter-organizational productivity and quality (Popoiu, Grosseck, Holotescu, 2012)
  9. 9. Privacy in Ecommerce • Ecommerce activities: • Administration • Legislation for intellectual property protection • Confidentiality • Safety • All these activities require user personal data that is stored in databases on web servers • Private information is collected automatically exploring the customer preferences • Information is sold to commerce and advertising companies • Customers are in scope of spam and malware activities, hack and fishing attacks (A. Cavoukian)
  10. 10. Privacy in Ecommerce • From the side of selling and business companies, personal data can be protected through combination of security actions: • availability of policy about collection and usage of personal data • collection and storage just of needed data • passwords protection of business computers • firewall installation • restriction access to private data to authorized employees • sending advertising emails to customers after permission to contact them • using of privacy seal program • use of secure servers and SSL encrypting technology • securing of hosting services (e-Business Toolkit, 2013) • Customer: • authorization, authentication, secure transactions • not just technical solutions, but also social, organizational, regulatory, economical (Ackerman, Davis), and educational approaches
  11. 11. Privacy in eGovernment • Steps for securing eGovernment systems: • development of policy strategy • realization of secure components and systems • institutional supervision of security • building a knowledge base “Privacy by Design” • applying anonymization techniques for minimal data usage • using of technical and legal approaches to avoid privacy risks when re- identification of previously anonymized data is performed • achievement of interoperability of different security tools (Jacobi, Jensen, Kool, Munnichs, Weber, 2013)
  12. 12. Privacy • There are a wide variety of models proposing solutions for protection of private data and combining components of different measures and actions • It allows usage just of general data, hiding unnecessary information, reaching pseudonymity or full anonymity
  13. 13. Measures to protect users’ privacy Data privacy Economic measures Social measures Legal measures Institutional measures Technical measures Operational measures Privacy as an economic good, markets for private data Collecting, storing and using just needed data, anonymity, social engineering Legal rules, norms, regulations at national, European and international level Legal rules, policy within the framework of companies, organizations Information system, server security, restricted access, secure transactions Suitable combination of different measures Educational measures Educate to share just needed data, other to keep private
  14. 14. Privacy in eLearning Privacy aspects in eLearning Data privacy to protect learners Data privacy to improve learning Learner keeps information private Third party keeps private data of others
  15. 15. Privacy in eLearning • Privacy risks • use of unsecure TCP/IP protocols • use of higher level of protocols like HTTP, SMTP, POP3, NNTP, browser’s chattering, existing of invisible hyperlinks, cookies, implementation of browsers (Working document of EC) • email communication mechanisms and web surfing process • privacy violations in organized Massive Open Online Courses (MOOCs) initiatives where the enrolled students are subjects to a mass data collection • online applications and services, communications platforms and business models in context of cloud computing, search engines, social networks, mobile Internet (Mendel, Puddephatt, Wagner, Hawtin and Torres, 2012)
  16. 16. Privacy in eLearning • The violations: • unauthorized access to data, stored in records with learner history • unappropriated use of stored students’ generated content on institutional servers • usage of antivirus program with possibility to collect students’ data (Weippl and Ebner, 2008) • Web 2.0 software, placed on different servers in different countries • records of students enrolled in MOOCs (Kolowichhttp,2014)
  17. 17. Privacy in eLearning • Solutions • eLearning environment to allow: • pseudonymity • anonymity • information sharing based on trust • giving cues with information about the actor role in a given context • allowing cues with verbal and non-verbal information • system has to give signal when a user much information shares • to announce the right current author of the information • system should educate in privacy • bad behavior of students should be punished (Anwar, Greer, and Brooks, 2006)
  18. 18. Privacy in eLearning • Collaborative and privacy-aware eLearning platform BluES is specially developed to recognize the privacy issues (Borcea-Pfitzmann, Liesebach and Pfitzmann, 2010) • it guarantees private data to be processed from services in a minimal way • it keeps transactions with their data, allowing transparency
  19. 19. Privacy for learning improvement • All examples found in scientific papers - treat the role of anonymity to stimulate and motivate learning • Anonymity possesses disadvantages – will not be discussed here • In the following learning scenarios: peer-to-peer learning, blended learning, group and collaborative learning anonymity is an important factor for learning
  20. 20. Survey and Results • The survey is designed with two goals: • to understand the students’ opinion whether their privacy is violated in a learning process • the main factors that define privacy and successful learning to be extracted
  21. 21. Survey and Results • The questions are grouped in four categories: (1) use of educational software (including social networks for educational purposes) and sharing of private data (2) what private data an educator should know to manage a successful learning process (3) the role of intelligent technologies in learning process and their relationship with privacy (4) possibilities for students to decide what kind of private data to share
  22. 22. Survey and Results Students’ predisposition to share private data in educational software
  23. 23. Survey and Results Students’ predisposition to share educational background
  24. 24. Survey and Results Students’ predisposition to give information about learning preferences
  25. 25. Survey and Results Students’ answers about whether intelligent technologies could support their learning
  26. 26. Survey and Results Students’ answers about whether intelligent technologies violate their privacy space
  27. 27. Survey and Results Students’ opinion about whether they have to possess an opportunity to decide what kind of personal data to share for educational purposes
  28. 28. Survey and Results Students’ answers about the features of educational software allowing them to choose what kind of private data to share
  29. 29. Data Privacy Model in eLearning Privacy in eLearning Student side -what information share -what relationships make -how his computer / mobile device is protected Third parties side -University – keeps and shares just needed data with students’ agreement, secure information system and databases -Educator – operate with minimal personal data, knowing of learning preferences, styles, learning progress -Other students – use the shared data -Administrator – indirect involved, keeps private data Educational software -Ensuring pseudonymity/ anonymity -Tools for sharing on trust -Options for choice of sharing -To educate in privacy -To give hint when much data are shared -Password protection -other technical issues
  30. 30. Conclusion • Data privacy should be reached after applying a suitable combination of measures • The main components in the model - proper attitudes of students and third parties to data keeping and ensuring of secure computer and information systems • Educational software should offer several tools in support of data sharing, tools for using just the necessary private data, tools for privileges protection • In eLearning privacy the principle: sharing on trust is important for the organization of a successful learning • Students want to trust educators, third parties and used educational software • Then the educators use the students’ private data to organize a learning process adequate to the student’s learning needs
  31. 31. • Thank you for your attention! Thanks for pictures: http://healthworkscollective.com/keithtullyy/309681/how-technology-affecting-healthcare http://www.datadesigngroup.com/wp-content/uploads/2014/12/ecommerce.png http://ec.europa.eu/digital-agenda/en/public-services https://e-estonia.com/wp-content/uploads/2014/04/eHealth.jpg http://pedemonts.com.au/service-solutions/e-commerce/ http://ucfretrolab.org/2012/09/29/anonymity-and-online-gaming/ http://www.vi.net/blog/2013/04/could-cloud-computing-become-just-computing/ http://www.truste.com/blog/

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