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USEMP - value of personal data CAISE 14 presentation

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The approach of USEMP project for the value of personal data CAISE 14 presentation@Thessaloniki

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USEMP - value of personal data CAISE 14 presentation

  1. 1. • A few words about VELTI • USEMP project & value of personal data • USEMP use cases related to value • USEMP framework for the value of personal data • USEMP LIO platform architecture
  2. 2. What VELTI does VELTI is a global provider of mobile marketing & advertising solutions that enable brands, advertising agencies, mobile operators, and media to engage with consumers via their mobile devices. Consumers’ personal data privacy protection & consumer consent is key to VELTI solutions VELTI conducts marketing campaigns in over 67 countries across the globe Most of the TOP-20 largest mobile operators worldwide have run campaigns with VELTI
  3. 3. Marketing & value of personal data Marketing is effective when products are offered to consumers that they are interested in them thus more prone to purchase them or some other related marketing objective Mobile & online marketing offers access to recorded personal data that can be used to infer consumers’ interest graph and the ability to offer them related information instead of broadcasting it (targeting) Value of personal data for brands/advertisers/marketers depends on: • how much it can increase the probability of a consumer purchasing their product or meeting the campaign objective • what is the perceived value of the marketing objective
  4. 4. • 3-year EU-funded R&D project to explore how to help & consumers understand the use of their personal data in Online Social Networks – http://www.usemp-project.eu • Consortium – Multimedia and semantic experts • CEA LIST (France); CERTH (Greece); – Social & Legal research experts/Living Labs • Iminds (Belgium); Radboud University (The Netherlands); Luleå University of Technology (Sweden); – Industrial partners • VELTI (Greece); HWC (United Kingdom);
  5. 5. • A large majority of Europeans engage with Online Social Networks (OSNs) – 74% of users consider that they do not have sufficient control – 70% are concerned with the way such data are handled by OSNs • Upcoming EU General Data Protection Regulation – harmonisation of EU’s legal framework and improvement of users’ control over their shared data • Asymmetry between data processing and control means available to OSNs and those afforded by citizens • Personal data sharing is a complex and pervasive process that is still not well understood • Work in different relevant fields is most often performed in isolation
  6. 6. • Objective: raising awareness about data shared online and improving user’s control of them • (a) Real-time OSN presence management – Development of semi-automatic privacy preservation tools – Joint analysis of volunteered, observed and inferred data • (b) Long-term OSN presence management – Visualisation tool which summarizes the privacy status – Controls for quick personal data visibility change
  7. 7. • Analyse the existing and proposed legal framework of privacy and data protection with regard to Online Social Networks (OSNs) • Advance the understanding of personal data handling through in-depth qualitative and quantitative user research • Create multimedia information mining tools adapted to personal information management • Build semi-automatic awareness tools to assist the users in their interaction with personal data • Contribute to the current debates related to the way personal data should be monetised • Propose an innovative living labs approach, adapted for personal data handling in OSNs
  8. 8. • Objective: assist the user in understanding the economic value of data shared online • (a) Awareness of the Economic Value of Personal Information – Modelling of the personal data monetisation process performed by OSNs – Contribution to the transparency of OSN business models • (b) Personal Content Licensing – Simulation of a framework for licensing personal information – Avoidance of commodification through an adapted rewards mechanism
  9. 9. • Objective: assist the user in understanding the economic value of data shared online • (a) Awareness of the Economic Value of Personal Information – Modelling of the personal data monetisation process performed by OSNs – Contribution to the transparency of OSN business models • (b) Personal Content Licensing – Simulation of a framework for licensing personal information – Avoidance of commodification through an adapted rewards mechanism
  10. 10. # Name Description Monetary Value A Demographics Personal details, eg. as Gender, Age, etc. High: advertisers wish to target users of certain demographic criteria B Psychological Traits Defined by psychologists (extraversion, openness, etc.) Low C Sexual Profile Relationship status, preferences, habits High: advertisers wish to target consumers based on their relationship status/lifestyle related to their sexual profile D Political Attitudes Supported politicians, parties and stance High: advertisers wish to target consumers based on the political affiliations since these are related to their general profile E Religious Beliefs & Cultural Heritage Religion (if any) and beliefs Moderate:advertisers wish to target consumers based on their religious and cultural beliefs F Health Factors & Condition Habits (e.g. smoking, drinking), medical conditions, health factors (exercise) High: advertisers wish to target consumers based on their habits G Location Characteristic locations of the individual and history of previous locations High: advertisers wish to target consumers based on their current location or their home location H Consumer Profile Preferred products and brands High: advertisers wish to target consumers based on their consumer profile attributes like the devices the use to access digital content
  11. 11. 3 approaches are explored further: collecting any explicit data about value ($) from records: f.e: how much advertisers/marketers actually pay for campaigns using personal data modeling users utility functions for their personal data & conducting focus groups/user tests: f.e: what are the consumers perceived value of their personal data collecting & computing indicators of value (scores) related to each consumer audience in Online Social network f.e: an Online Social network user with larger audience has larger value to other users (for audiences with similar attributes)
  12. 12. • OSN users roles: – user generated content producer – content consumer – part of a network of value • OSN users generating value activities – sharing content (images/posts) – interest graph/sharing preferences (likes/comments/follows/retweets/sharing) • this has a network effect since it allows to better estimate other members’ social graph interest graphs – sharing demographic/location/contextual personal data • this has a network effect since it generates also value for his network
  13. 13. Persons of interest tracking correlations classifications profiles conversion probabilities
  14. 14. As a producer of user generated content in a social graph I want to know what is the expected audience of my activities related to a category that maybe of interest to advertisers/marketers [the more estimated audience I have the more valuable my contribution would be] [Lio should be able to compute an OSN's user audience for different type of categories that maybe of interest to advertisers based on past behaviour, klout-type of algorithms can be used to estimate the audience] [Lio should have access to the social graph data of the consumer/producer of user generated content]
  15. 15. Weighted average of: • Twitter repeats and mentions • Facebook comments, wall posts and likes • Google+ comments, reshares and +1s • LinkedIn Comments and Likes • Foursquare tips
  16. 16. As a producer of user generated content I want to know what is the ranking of my content base [affinity, weight, time decay] what is have my comment shown to a friend to be consumed [each post/like from my network affect the ranking of my posts to my audience, the higher the rank to more of my audience the better] [Lio should be able to compute the potential ranking of a type of post] [Lio should be able to compute the potential ranking of a type of post] [Lio should have access to the social graph data of the consumer/producer of user generated content]
  17. 17. Edge rank basics ------------------------------------------------- • each user creates new objects e that will be displayed to the news feed page of each of his/her friends in the social graph • the original creator of the object is the owner of the object • other users interact with the object (share/comments/like) • each time a user other than the owner interacts with an object, a new edge is created for the object • edge (user that created it, type of interaction, time) ------------------------------------------------- the rank of an object e is defined: ------------------------------------------------- edgerank(object e) = sum (edge score, over all edges) each edge score is computed as the product of: • affinity score between the user that has created the action and the owner of the object • weight of the action • time decay
  18. 18. As a consumer/audience for advertisement/marketing campaigns I want to know how value-able my shared or non- shared personal data are in a given time for advertisers in a OSN [certain personal data maybe more valuable to advertisers than others depending on the demand/offer market type of dynamics] [Lio should be able to compute an indication of what is the CPM value for a campaign running on based on my share or NON- share personal data I have included in OSN] [Lio should have access to the advertising data/API of an OSN to estimate the value of CPM for different filters/classifications and to OSN user social graph ]
  19. 19. As a consumer of OSN application services I want to know how my shared data may be valued by different OSN services provider based on known values/classification schemes for application providers [different application providers classify their users in different classes, we need a reference for this] [Lio should provide my current ranking in terms of value for different industries] [Lio implementation requires access to OSN social graph data and additional external data from application providers, this needs to be explored further]

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