Listener Anonymizer: Camouflaging Play Logs to Preserve User’s Demographic Anonymity (ISMIR 2018)
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1) The document describes a technique called Listener Anonymizer that aims to camouflage users' music play logs to preserve their demographic anonymity from being predicted. 2) Listener Anonymizer works by computing the effectiveness of different songs at anonymizing a user's nationality, gender, or age based on their play log, and recommending songs to play that will anonymize their demographics. 3) By playing the recommended songs, it biases the prediction of a user's demographics from their play log, preserving their anonymity while still allowing them to use music recommendation services.
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Listener Anonymizer: Camouflaging Play Logs to Preserve User’s Demographic Anonymity (ISMIR 2018)
- 1. Listener Anonymizer: Camouflaging Play Logs to Preserve User’s Demographic Anonymity Kosetsu Tsukuda, Satoru Fukayama, Masataka Goto National Institute of Advanced Industrial Science and Technology (AIST), Japan Sept. 26, 2018
- 2. I love music recommendation Music recommendation can improve user’s music experience
- 3. To improve recommendation accuracy, it is beneficial to predict user’s demographic attributes (age, gender, nationality) A user’s demographics can be predicted with high accuracy by using the user’s play log 𝑡𝑡 Your nationality is Age Gender Nationality 4.13mean absolute error 77.01%accuracy 69.37%accuracy T. Krismayer, M. Schedl, P. Knees, R. Rabiser Prediction of User Demographics from Music Listening Habits CBMI 2017 Play log
- 4. Technique to leverage play logs for predicting users' demographic attributes ? COUNTERBALANCE
- 5. Technique to leverage play logs for predicting users' demographic attributes Technique to camouflage play logs for preserving users' demographic anonymity COUNTERBALANCE
- 7. 𝑡𝑡 Emma is a 22-year-old French female She uses both an online music service and Listener Anonymizer She concealed her nationality when she signed up to the service
- 8. 𝑡𝑡 Listener Anonymizer … Compute a probability distribution Emma is a 22-year-old French female She uses both an online music service and Listener Anonymizer She concealed her nationality when she signed up to the service
- 9. 𝑡𝑡 Listener Anonymizer Compute a probability distribution … Emma is a 22-year-old French female She uses both an online music service and Listener Anonymizer She concealed her nationality when she signed up to the service
- 10. 𝑡𝑡 Listener Anonymizer Compute a probability distribution … Emma is a 22-year-old French female She uses both an online music service and Listener Anonymizer She concealed her nationality when she signed up to the service
- 11. 𝑡𝑡 Listener Anonymizer Compute a probability distribution … … Emma is a 22-year-old French female She uses both an online music service and Listener Anonymizer She concealed her nationality when she signed up to the service
- 12. 𝑡𝑡 Listener Anonymizer Compute a probability distribution … Listener Anonymizer Your nationality can be predicted as French with a probability of 67% Anonymize … Emma is a 22-year-old French female She uses both an online music service and Listener Anonymizer She concealed her nationality when she signed up to the service
- 13. 𝑡𝑡 Listener Anonymizer Compute a probability distribution … … 0.21 0.04 0.77 0.89 0.48 0.92 0.25 0.33 0.82 0.29 0.46 0.86 𝑢𝑢1 𝑢𝑢2 𝑢𝑢𝑟𝑟 𝑠𝑠1 𝑠𝑠2 𝑠𝑠3 𝑠𝑠𝑚𝑚… … Compute the effectiveness of each song to anonymize her nationality 𝑢𝑢𝑖𝑖: user 𝑠𝑠𝑗𝑗: song Emma is a 22-year-old French female She uses both an online music service and Listener Anonymizer She concealed her nationality when she signed up to the service
- 14. 𝑡𝑡 Listener Anonymizer Compute a probability distribution … … 0.21 0.04 0.77 0.89 0.48 0.92 0.25 0.33 0.82 0.29 0.46 0.86 𝑢𝑢1 𝑢𝑢2 𝑢𝑢𝑟𝑟 𝑠𝑠1 𝑠𝑠2 𝑠𝑠3 𝑠𝑠𝑚𝑚… … Compute the effectiveness of each song to anonymize her nationality Listener Anonymizer Recommendations: Play 𝑢𝑢𝑖𝑖: user 𝑠𝑠𝑗𝑗: song Emma is a 22-year-old French female She uses both an online music service and Listener Anonymizer She concealed her nationality when she signed up to the service
- 15. 𝑡𝑡 Listener Anonymizer Compute a probability distribution … … 0.21 0.04 0.77 0.89 0.48 0.92 0.25 0.33 0.82 0.29 0.46 0.86 𝑢𝑢1 𝑢𝑢2 𝑢𝑢𝑟𝑟 𝑠𝑠1 𝑠𝑠2 𝑠𝑠3 𝑠𝑠𝑚𝑚… … Compute the effectiveness of each song to anonymize her nationality 𝑢𝑢𝑖𝑖: user 𝑠𝑠𝑗𝑗: song Emma is a 22-year-old French female She uses both an online music service and Listener Anonymizer She concealed her nationality when she signed up to the service
- 16. … 𝑡𝑡 Listener Anonymizer Compute a probability distribution … 0.21 0.04 0.77 0.89 0.48 0.92 0.25 0.33 0.82 0.29 0.46 0.86 𝑢𝑢1 𝑢𝑢2 𝑢𝑢𝑟𝑟 𝑠𝑠1 𝑠𝑠2 𝑠𝑠3 𝑠𝑠𝑚𝑚… … Compute the effectiveness of each song to anonymize her nationality Your nationality is … ?? 𝑢𝑢𝑖𝑖: user 𝑠𝑠𝑗𝑗: song Emma is a 22-year-old French female She uses both an online music service and Listener Anonymizer She concealed her nationality when she signed up to the service
- 17. Age Gender Nationality 3.22songs 9.28songs 4.36songs 𝑡𝑡 … Avg number of songs for camouflaging play logs 30 songs
- 18. Probability When a user plays 30 songs, the distribution is strongly biased to Polish (the left most graph) She can camouflage her play log by playing only three songs recommended by Listener Anonymizer
- 19. I can enjoy music while preserving my demographic anonymity! Without Listener Anonymizer With Listener Anonymizer It is important to show that preserving users’ demographic anonymity is technically possible Listener Anonymizer gives a choice to a user Demographic anonymity High rec. accuracy I do not care about my demographic anonymity!
- 20. Listener Anonymizer might degrade recommendation accuracy We dared to propose this controversial approach to raise privacy issues in the ISMIR community