Palo Alto Research Center Inc.

Mitsubishi Electric Research Laboratories, Inc.

Manas Ashok Pathak

Shantanu Rane

Cong Wang

Ersin Uzun

Kurt Edward Partridge

Richard Chow

Bhiksha Ramakrishnan

Wei Sun

Runting Shi

Bhiksha Raj

A smart coupon-delivery system provides targeted coupons to a user using a criteria-encoded message received from a coupon server. The criteria-encoded message is generated by the coupon server from a behavior-criteria vector that indicates criteria for selecting a recipient of a digital coupon. The system then generates a user-behavior vector to indicate one or more behavior patterns of an end-user, and encodes the user-behavior vector to produce a behavior-encoded message. The system then determines whether the end-user is eligible to receive the digital coupon based on the criteria-encoded message and the behavior-encoded message. If the system determines that the end-user is eligible, the system presents the digital coupon to the end-user.

Privacy-preserving behavior targeting for digital coupons

A recommender system can generate a predicted item rating for one user by performing collaborative filtering on item ratings from other users. The recommender system can include a client device that interfaces with a server to obtain a predicted item rating for a local user. The client device can generate a standardized ratings vector for the user, and computes a group identifier for the user based on the standardized ratings vector. The system also generates a noisy ratings vector for the local user, and sends a user-ratings snapshot to a recommendation server that includes the group identifier and the noisy ratings vector. The recommender system can also include the recommendation server that generates a predicted item rating for the user by performing collaborative filtering on ratings vectors from a plurality of other users that belong to the same ratings group.

Privacy-preserving collaborative filtering

A probability of an observation sequence stored at a client is evaluated securely with respect to a hidden Markov model (HMM) stored at a server. The server determines, for each state of the HMM, an encryption of a log-probability of a current element of the observation sequence. Determines, for each state of the HMM, an encryption of a log-summation of a product of a likelihood of the observation sequence based on a previous element of the observation sequence and a transition probability to the state of the HMM. Determines an encryption of a log-likelihood of the observation sequence for each state as a product of the encryption of a log-summation and an encryption of a corresponding log-probability of the current element of the observation sequence; and determines an encryption of the log-probability of the observation sequence based on the log-likelihood of the observation sequence for each state.

Privacy-preserving probabilistic inference based on hidden Markov models

Embodiments of the invention disclose a system and a method for determining a differentially private aggregate classifier for a set of databases, wherein each database in the set of databases is associated with a classifier and a noise value, wherein the classifier and the noise value are determined locally for each database, such that a combination of the classifier and the noise value ensure a differential data privacy of the database, and wherein the differentially private aggregate classifier preserves the differential data privacy of each database. The differentially private aggregate classifier is a combination of the classifiers of the set of databases modified with the noise value corresponding to a smallest database in the set of databases.

Differentially private aggregate classifier for multiple databases

Growing community of inventors

Pittsburgh, PA, United States of America

Manas Ashok Pathak