1. BlurMe: Inferring and Obfuscating
User Gender Based on Ratings
Smriti Bhagat
Joint work with Udi Weinsberg, Stratis Ioannidis, Nina Taft
Technicolor, Palo Alto

2. Motivation
What alldo
What can
they know
infer
about me!!?!
about me?
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3. Overview
Given a rating profile, can we infer the user s demographics – age,
gender, ethnicity, income etc.?
If so, is it possible to obfuscate a user’s gender while maintaining
useful recommendations?
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4. Gender Inference
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5. System Setting
Recommender
System
Gender Male or
Inference Female
Training
Dataset
(Ratings & Gender User Rating
of users) Profile
(Obfuscated)
Gender
Obfuscation
User Rating
Profile
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6. Gender Inference – Methods
Notation: For a user we wish to classify, let
• : vector of user ratings for M movies (0 if not rated)
• : indicator vector of movies rated
• : user gender
Classification:
• Three Generative Models
• Binary Logistic Regression
• Support Vector Machines (SVM)
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7. Gender Inference – Generative Models (1)
Notation: For a user we wish to classify, let
• : : vector of user ratings for M movies (0 if not rated)
• : indicator vector of movies rated
• : user gender
Classification:
1. Bernoulli Naïve Bayes: Over indicator vector of movies rated
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8. Gender Inference – Generative Models (2)
Notation: For a user we wish to classify, let
• : : vector of user ratings for M movies (0 if not rated)
• : indicator vector of movies rated
• : user gender
Classification:
2. Multinomial Naïve Bayes: Over user ratings vector
,
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9. Gender Inference – Generative Models (3)
Notation: For a user we wish to classify, let
• : : vector of user ratings for M movies (0 if not rated)
• : indicator vector of movies rated
• : user gender
Classification:
3. Mixed Naïve Bayes: Considers both indicator and ratings vector
Bernoulli Normal
distribution distribution
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10. Gender Inference – Logistic Regression & SVMs
Notation: For a user we wish to classify, let
• : vector of user ratings for M movies (0 if not rated)
• : indicator vector of movies rated
• : user gender
Classification:
• Logistic regression: Movies may not be rated independently
Over and Indicating how correlated a
movie is with the inferred
gender
• SVM: Over and
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11. Gender Inference Results: AUC comparison
Datasets:
Flixster 34k users, 17k movies, 5.8m ratings, M:F=4:6
Movielens 6k users, 3.7k movies, 1m ratings, M:F=7:3
Flixster Movielens
Logistic regression and SVM outperform naïve bayes methods
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12. Gender Inference Results: Rating event
Classifier Flixster Movielens
AUC (Precision/Recall) AUC (Precision/Recall)
SVM 0.82 (0.73/0.70) 0.86 (0.78/0.77)
SVM 0.80 (0.72/0.70) 0.85 (0.78/0.77)
Logistic Regression 0.84 (0.76/0.77) 0.85 (0.80/0.80)
Logistic Regression 0.83 (0.75/0.76) 0.84 (0.78/0.79)
Rating event is a strong signal for classification
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13. Gender Inference Results: Most indicative movies
List of top-10 movies most indicative of a user’s gender
Flixster
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14. Gender Obfuscation
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15. System Setting
Recommender
System
Gender
Training Inference
Dataset
(User Ratings + Obfuscated
Gender) Rating Profile
Gender
Obfuscation
User Rating
Profile
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16. Gender Obfuscation: Main Idea
• Insert k additional ratings, such that, it is hard to infer the
gender of the user, while minimally impacting the quality of
recommendations received
• Two key decisions:
Which movies
should be added?
What should be the rating
assigned to each movie?
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17. Gender Obfuscation: Mechanisms
Q: Which movie to add?
A: Three strategies for movie selection:
• Random Strategy
• Pick an movie uniformly at random from the list of movies
indicative of the opposite gender
• Sampled Strategy
• Sample a movie based on the distribution of scores that indicate
how strongly correlated a movie is with the opposite gender
• Greedy Strategy
• Greedily pick the movie most correlated with the opposite gender
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18. Gender Obfuscation: Rating Assignment
Q: What should be the rating?
A: Two rating assignments:
• Average movie rating
• Predicted rating (using user and movie latent factors)
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19. Gender Obfuscation: Accuracy of Inference
Flixster
Compared with 76.5% gender inference accuracy on unadulterated data,
using the greedy strategy to add
• 1% ratings drops the accuracy to 15% (80% decrease)
• 10% ratings drops the accuracy to 0.1%
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20. Gender Obfuscation: Impact on Recommendations
Flixster
Overall change in RMSE is small, i.e., maximum of 0.015 when 10% extra
ratings are added using random strategy
In theory, change in RMSE can be 0 if the rating value is the user’s
predicted rating
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21. Gender Obfuscation: Privacy-Utility Tradeoff
Utility decreases
Flixster
Privacy increases
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22. Conclusions
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23. Contributions
• Ratings alone can predict gender with about 80% accuracy
• Act of rating a movie, regardless of the rating provided, is
indicative of gender
• Obfuscation strategies proposed reduce the success of gender
inference by 80% with adding just 1% ratings while resulting in
an insignificant change in RMSE
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24. Ongoing work
• Repeat for other demographic attributes – age, ethnicity
• Inference in an online setting – which movie ratings should be
solicited from a user to quickly infer demographics
• Design obfuscation mechanisms that provide theoretical
guarantees on privacy against any statistical inference
mechanism
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25. Thank You!
Intern, post-doc or join our research team
@paris or @palo-alto #technicolor # recsys2012
Contact: smriti.bhagat@technicolor.com
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