Listener Anonymizer: Camouflaging Play Logs to Preserve User’s Demographic Anonymity (ISMIR 2018)
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Listener Anonymizer: Camouflaging Play Logs to Preserve User’s Demographic Anonymity (ISMIR 2018)

  1. 1. Definition of Anonymity How can users enjoy music when using an online music service while preserving their demographic anonymity? We listed up five factors to consider for realizing Listener Anonymizer. Not-first-anonymity … k-flat-anonymity … The probability of French is not the highest. Top k nationalities have almost the same probabilities. 2. Method for Predicting Demographics  It is common to use music metadata extracted from the user’s play log  The state-of-the-art method is the one proposed by Krismayer et al[1]. 𝑡𝑡 10,000 dims for artists 10,000 dims for artist’s tags PCA SVM … [1] Prediction of User Demographics from Music Listening Habits T. Krismayer, M. Schedl, P. Knees, R. Rabiser CBMI 2017 3. Timing of Anonymization 4. User’s True Demographics 5. Multiple Demographics When anonymity is no longer satisfied When a user does not use a smartphone Z Z The system is required to select as few songs as possible so that the user can soon resume listening to her favorite songs. Some users still hope to play as few recommended songs as possible to save on the packet communication fee. When the system does not know it When the system knows it ? ALERT ALERT Alert is displayed with proper timing. The user will tell her true demographic if the system is a stand-alone application. In exchange for complete anonymity, songs will be frequently recommended: this is a heavy burden for her. ♀20’s … ♀ ♂ … 30’s 20’s 50’s When a user tells many demographics that she wants to preserve the anonymity of, alerts may frequently be displayed. 1. Counterbalance METHOD EVALUATION DISCUSSION FACTORS We are fully aware of the importance and usefulness of music recommendation. We dared to propose this controversial approach to raise privacy issues in the ISMIR community. … 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 … …  Detect the nationality having the second-highest probability (e.g., German)  Collect the top 𝑟𝑟 users in terms of the probability of a user on German  Remove the 𝑙𝑙th song from 𝑢𝑢’s play log and compute the new probability by using the remaining 𝑚𝑚-1 songs  Collect the top c artists based on the probability gap  32,991 users with ≥ 500 play logs from dataset  For each user, use the first 30 songs from the oldest songs in the play logs (i.e., 𝑚𝑚 = 30)  Factors - Not-first-anonymity - Camouflage the play log with as few songs as possible - The system knows the user’s true demographic - Single demographic anonymity Age Gender Nationality Random 15.34 27.18 17.60 Popularity 24.10 29.44 29.42 Proposed 3.22 9.28 4.36 2. Give a choice to a user 3. User’s Taste in Music Recommendation accuracy Anonymization When Listener Anonymizer recommends songs, if they match the user’s taste in music, she will not be reluctant to keep listening to the songs. Reflecting user’s taste would also be beneficial to satisfy both anonymization and good recommendation. 0.32 1.59 1.44 0.86 …… 2.02 0.71 … 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) 𝑚𝑚: #songs in a user’s play log 𝑢𝑢: user 𝑠𝑠: song  It is important to show that preserving users’ demographic attributes is technically possible  Listener Anonymizer gives a choice to a user  It is beneficial to predict users’ demographic attributes  It is also important to think about a counterbalance Technique to leverage play logs for predicting demographic attr. Technique to camouflage play logs for preserving demographic attr. I can enjoy music while preserving my demographic anonymity! Without Listener Anonymizer With Listener Anonymizer High rec. accuracy I do not care about my demographic anonymity! Demographic anonymity (songs) Compute the effectiveness of each candidate song to anonymize the user’s demographic attribute Results: avg numbers of songs for camouflaging play logs (the smaller, the better) [ISMIR 2018]