Differential privacy preserving recommendation
First Claim
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1. A method for providing differential privacy comprising:
- receiving user rating data at a correlation engine through a network, the user rating data comprising ratings generated by a plurality of users for a plurality of items;
removing per-item global effects from the user rating data by;
calculating an average rating for each item rated in the user rating data;
determining a plurality of fictitious ratings for each item rated in the user rating data, wherein each fictitious rating of an item is set to the calculated average rating of the item;
calculating a stabilized average rating for each item rated in the user rating data using the ratings in the user rating data for the item and the plurality of fictitious ratings for the item; and
for each rating in the user rating data, subtracting the calculated stabilized average rating for the rated item from the rating;
generating correlation data from the user rating data by the correlation engine, the correlation data identifying correlations between the items based on the user generated ratings;
generating noise by the correlation engine; and
adding the generated noise to the generated correlation data by the correlation engine to provide differential privacy protection to the generated correlation data.
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Abstract
User rating data may be received at a correlation engine through a network. The user rating data may include ratings generated by a plurality of users for a plurality of items. Correlation data may be generated from the received user rating data by the correlation engine. The correlation data may identify correlations between the items based on the user generated ratings. Noise may be generated by the correlation engine, and the generated noise may be added to the generated correlation data by the correlation engine to provide differential privacy protection to the user rating data.
22 Citations
19 Claims
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1. A method for providing differential privacy comprising:
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receiving user rating data at a correlation engine through a network, the user rating data comprising ratings generated by a plurality of users for a plurality of items; removing per-item global effects from the user rating data by; calculating an average rating for each item rated in the user rating data; determining a plurality of fictitious ratings for each item rated in the user rating data, wherein each fictitious rating of an item is set to the calculated average rating of the item; calculating a stabilized average rating for each item rated in the user rating data using the ratings in the user rating data for the item and the plurality of fictitious ratings for the item; and for each rating in the user rating data, subtracting the calculated stabilized average rating for the rated item from the rating; generating correlation data from the user rating data by the correlation engine, the correlation data identifying correlations between the items based on the user generated ratings; generating noise by the correlation engine; and adding the generated noise to the generated correlation data by the correlation engine to provide differential privacy protection to the generated correlation data. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A system for providing differential privacy comprising:
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a computing device; a correlation engine adapted to; receive user rating data, wherein the user rating data comprises a plurality of item ratings generated by a plurality of users; remove per-item global effects from the user rating data by; calculating an average rating for each item rated in the user rating data; determining a plurality of fictitious ratings for each item rated in the user rating data, wherein each fictitious rating of an item is set to the calculated average rating of the item; calculating a stabilized average rating for each item rated in the user rating data using the ratings in the user rating data for the item and the plurality of fictitious ratings for the item; and for each rating in the user rating data, subtracting the calculated stabilized average rating for the rated item from the rating; generate a covariance matrix from the user rating data; add noise to the generated covariance matrix to provide differential privacy protection to the covariance matrix; and publish the generated covariance matrix; and a recommendation engine adapted to; receive the generated covariance matrix; and generate item recommendations using the published covariance matrix. - View Dependent Claims (11, 12, 13, 14, 15)
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16. A method for providing differential privacy comprising:
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receiving user rating data by a correlation engine through a network, wherein the user rating data comprises a plurality of ratings of items generated by a plurality of users; removing per-item global effects from the user rating data by the correlation engine by; calculating an average rating for each item rated in the user rating data; determining a plurality of fictitious ratings for each item rated in the user rating data, wherein each fictitious rating of an item is set to the calculated average rating of the item; calculating a stabilized average rating for each item rated in the user rating data using the ratings in the user rating data for the item and the plurality of fictitious ratings for the item; and for each rating in the user rating data, subtracting the calculated stabilized average rating for the rated item from the rating; generating a covariance matrix from the user rating data by the correlation engine; adding noise to the generated covariance matrix to provide differential privacy protection to the user rating data by the correlation engine; and publishing the generated covariance matrix by the correlation engine. - View Dependent Claims (17, 18, 19)
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Specification