Abstract: A method to obfuscate the accurate detection of demographic information of a new user who provides ratings to a digital content service having a recommender system includes training an inference engine to detect the 5 demographic information. The training set includes movie ratings and demographic information from a plurality of other users. The new user inputs ratings, such as movie ratings, and the inference engine determines the demographic information of the new user. An obfuscation engine then adds movie ratings to the recommender system which cause an inference engine in the recommender system to fail to accurately detect the demographic information of the new user.

Abstract: A method and system for privacy-preserving ridge regression using partially homomorphic encryption and masks is provided. The method includes the steps of requesting a garbled circuit from a crypto service provider, collecting data from multiple users that has been formatted and encrypted using partially homomorphic encryption, summing the data that has been formatted and encrypted using partially homomorphic encryption, applying a prepared masks to the summed data, receiving garbled inputs corresponding to prepared mask from the crypto service provider using oblivious transfer, and evaluating the garbled circuit from the crypto service provider using the garbled inputs and masked data.

Abstract: A hybrid approach to privacy-preserving ridge regression is presented that uses both homomorphic encryption and Yao garbled circuits. Users in the system submit their data encrypted under a linearly homomorphic encryption. The linear homomorphism is used to carry out the first phase of the algorithm that requires only linear operations. The output of this phase generates encrypted data, in a form that is independent of the number of users n. In a second phase, a Yao garbled circuit that first implements homomorphic decryption and then does the rest of the regression algorithm (as shown, an optimized realization can avoid decryption in the garbled circuit) is evaluated. For this step a Yao garbled circuit approach is much faster than current fully homomorphic encryption schemes. Thus the best of both worlds is obtained by using linear homomorphisms to handle a large data set and using garbled circuits for the heavy non-linear part of the computation.

Abstract: A user has an inherent predisposition to have an interest for a particular item. The user's interests may also be affected by what people in her social circle are interested in. To more accurately make recommendations, a user's inherent interests, social influence, how a user responds to recommendations, and/or the user's desire for novelty are taken into consideration. Considering the evolution of users' interests in response to the users' social interactions and users' interactions with the recommender system, the recommendation problem is formulated as an optimization problem to maximize the overall expected utilities of the recommender system. Tractable solutions to the optimization problem are presented for some use cases: (1) when the system does not perform personalization; (2) when the users in the system exhibit attraction dominant behavior; and (3) when the users in the system exhibit aversion dominant behavior.

Abstract: A method and system for privacy-preserving ridge regression using masks is provided. The method includes the steps of requesting a garbled circuit from a crypto service provider, collecting data from multiple users that has been formatted and encrypted using homomorphic encryption, summing the data that has been formatted and encrypted using homomorphic encryption, applying prepared masks to the summed data, receiving garbled inputs corresponding to prepared mask from the crypto service provider using oblivious transfer, and evaluating the garbled circuit from the crypto service provider using the garbled inputs and masked data.

Abstract: A method to detect a number of individual users included in a composite set of movie ratings having ratings from a plurality of individual users includes accessing the composite set of movie ratings and movie profiles and loading the composite set and movie profiles into a rating analysis engine. Processing the composite set along with the movie profiles determines a number of partitions present in the composite set, wherein the number of partitions is determined iteratively using subspace clustering of ratings from the composite set. The determined number of partitions is output and corresponds to the number of individual users included in the composite set of movie ratings.

Abstract: An apparatus and a method for designing a data market experiment given a fixed budget and a set of potential subjects for the experiment are described. An experimenter human subject, or any other kind of experiment through which it collects data, and can incentivize the participation of subjects in the experiment through monetary compensation. The experimenter observes some publicly known information about the subjects, as well as the money each potential subject requests to participate in the experiment. Based on this information, the method determines which users to pay, and how much, to participate in the experiment. The method views experimental design in a strategic setting, by studying mechanism design issues, such as incentivizing users to report a truthful value for their data. The method has the following properties of being budget feasible, computationally tractable, nearly-optimal, and truthful in that the subjects have no incentive to declare desired compensations that are untruthful.

Abstract: The current methods and apparatus provide a system that learns a private attribute, such as gender, based on at least one iteration of presenting an item to a user and receiving ratings from the user for this item. In an exemplary embodiment, the system may solicit ratings for strategically selected items, such as movies for example, and then infers the user's gender. Based on the assessed confidence in the demographic selected, the system may repeat the selection, presentation and ratings of another item. The proposed system can strategically select the sequence of items that are presented to the user for a rating. By selecting the next item to be rated based on a maximum posterior probability confidence, a demographic with a certain threshold of confidence can be inferred. The inventive arrangements are based on novel usage of Bayesian matrix factorization in an active learning setting.

Abstract: A method, apparatus and system for microphone array calibration include recording audio using at least two microphones, which comprise an array of microphones, using a target microphone of the array of microphones, determining an attenuation factor for audio originating from respective locations of other microphones of the array of microphones, using a target microphone of the array of microphones, determining a delay factor for audio originating from respective locations of other microphones of the array of microphones and implementing the determined attenuation factor and delay factor for removing audio originating from respective locations of the other microphones of the array of microphones from an audio signal captured by the target microphone. The method, apparatus and system then further include removing audio originating from respective locations of the other microphones of the array of microphones from an audio signal captured by the target microphone using beam-forming techniques.

Abstract: Existing recommendation systems leverage user social and demographic information, e.g., age, gender and political affiliation, to personalize content and make recommendations. However, users do not volunteer this information due to privacy concerns or to the lack of initiative in filling out their profile information. The current methods and apparatus provide principles in which the system may learn the private attribute for those users who do not voluntarily disclose them. In an exemplary embodiment, the system receives ratings for items, such as movies, for example, that may be used by a recommendation system. The inventive arrangements are based on novel usage of Bayesian matrix factorization in an active learning setting. Such a system can be carried out using significantly fewer rated items than previously proposed static inference methods. The system functions effectively without sacrificing the quality of the regular recommendations made to the user.

Abstract: A method to determine demographic information of a new user utilizing only ratings includes training an inference engine informed with a training data set which includes ratings and demographic information from a plurality of other users. The new user inputs ratings, such as movie ratings, and an inference engine determines demographic information of the new user. The demographic information of the new user can then be used to provide recommendations or to provide targeted advertisements to the new user.

Abstract: A method of selection that maximizes an expected reward in a contextual multi-armed bandit setting gathers rewards from randomly selected items in a database of items, where the items correspond to arms in a contextual multi-armed bandit setting. Initially, an item is selected at random and is transmitted to a user device which generates a reward. The items and resulting rewards are recorded. Subsequently, a context is generated by the user device which causes a learning and selection engine to calculate an estimate for each arm in the specific context, the estimate calculated using the recorded items and resulting rewards. Using the estimate, an item from the database is selected and transferred to the user device. The selected item is chosen to maximize a probability of a reward from the user device.

Abstract: A method for controlling the average time a peer spends in a swarm of peers in a file sharing system includes first establishing an autonomous mode of operation in the swarm of peers. In the autonomous mode, peers communicate only with other peers in their swarm in order to gain access to pieces of a desired file. If the swarm size meets a threshold size, then the file sharing system switches to a universal mode. In a universal mode, peers from one swarm are permitted to exchange desired file pieces with other peers in other swarms. If the desired file pieces held by peers within a swarm meets a threshold number, then the file sharing system transitions back to the autonomous mode of operation.

Abstract: A method for generating a recommendation item for members of a group includes registering a plurality of users as members of the group, identifying a subgroup of members of the group of users wherein the subgroup requests a recommendation item from a recommendation engine, calculating, using a multi-armed bandit algorithm, a recommendation item for the subgroup of members. The recommendation item is provided to the subgroup members for their evaluation. After evaluating the recommendation item, the individual users rate the recommendation item which updates the recommendation engine with preferences representing the members of the subgroup.

Abstract: Disclosed are methods and apparatus for identifying users of content. The methods include identifying contextual information of a group of users, gathering user access data of the users on the basis of the contextual information of the group of users, analyzing temporal information of the user access data, and identifying particular users in the group of users on the basis of the analyzed temporal information and the contextual information.