Abstract: A system for displaying product information to a customer is described herein. The system includes a processor that is programmed to receive search data in response to a product search request received from the customer and retrieve product records associated with the search data from a database. Each product record is associated with a corresponding product and includes a product description and a plurality of item attributes having corresponding attribute values. The processor selects a set of the item attributes and generates a product summary for each of the products as a function of the selected set of item attributes. Each product summary includes corresponding attribute values associated with the selected set of item attributes. The processor generates and displays product information associated with each of the products being included in the search data. The associated product information includes a corresponding product summary and a corresponding product description.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.