Abstract: Technologies are disclosed for computing heavy hitter histograms using locally private randomization. Under this strategy, “agents” can each hold a “type” derived from a large dictionary. By performing an algorithm, an estimate of the distribution of data can be obtained. Two algorithms implement embodiments for performing methods involving differential privacy for one or more users, and usually are run in the local model. This means that information is collected from the agents with added noise to hide the agents' individual contributions to the histogram. The result is an accurate enough estimate of the histogram for commercial or other applications relating to the data collection of one or more agents. Specifically, the proposed algorithms improve on the performance (measured in computation and memory requirements at the server and the agent, as well as communication volume) of previously solutions.

Abstract: Technologies are provided for efficiently querying a database using a plurality of oblivious random-access memories (ORAMs) while providing differential privacy. Subsets of a set of database records can be stored in a plurality of ORAMs. The subsets of database records in the separate ORAMs can be concurrently accessed (for example, by a database query server). When a database query is received, a number of database records that match the query can be identified for each of the ORAMs. A differential privacy constraint can be used to determine an additional number of database records to be retrieved from each ORAM. The differential privacy constraint can specify an upper bound on the number of records to be retrieved from each ORAM to prevent (or reduce the risk of) information leakage. Once all of the identified records are retrieved from the plurality of ORAMs, the additional records can be discarded.

Abstract: Technologies are provided for efficiently querying a database using a plurality of oblivious random-access memories (ORAMs) while providing differential privacy. Subsets of a set of database records can be stored in a plurality of ORAMs. The subsets of database records in the separate ORAMs can be concurrently accessed (for example, by a database query server). When a database query is received, a number of database records that match the query can be identified for each of the ORAMs. A differential privacy constraint can be used to determine an additional number of database records to be retrieved from each ORAM. The differential privacy constraint can specify an upper bound on the number of records to be retrieved from each ORAM to prevent (or reduce the risk of) information leakage. Once all of the identified records are retrieved from the plurality of ORAMs, the additional records can be discarded.

Abstract: Technologies are disclosed for computing heavy hitter histograms using locally private randomization. Under this strategy, “agents” can each hold a “type” derived from a large dictionary. By performing an algorithm, an estimate of the distribution of data can be obtained. Two algorithms implement embodiments for performing methods involving differential privacy for one or more users, and usually are run in the local model. This means that information is collected from the agents with added noise to hide the agents' individual contributions to the histogram. The result is an accurate enough estimate of the histogram for commercial or other applications relating to the data collection of one or more agents. Specifically, the proposed algorithms improve on the performance (measured in computation and memory requirements at the server and the agent, as well as communication volume) of previously solutions.

Abstract: A database has a plurality of entries and a plurality of attributes common to each entry, where each entry corresponds to an individual. A query is received from a querying entity query and is passed to the database, and an answer is received in response. An amount of noise is generated and added to the answer to result in an obscured answer, and the obscured answer is returned to the querying entity. The noise is normally distributed around zero with a particular variance. The variance R may be determined in accordance with R>8 T log2(T/?)/?2, where T is the permitted number of queries T, ? is the utter failure probability, and ? is the largest admissible increase in confidence. Thus, a level of protection of privacy is provided to each individual represented within the database. Example noise generation techniques, systems, and methods may be used for privacy preservation in such areas as k means, principal component analysis, statistical query learning models, and perceptron algorithms.

Abstract: An addition chain is first generated, and then an integer x is derived from it. Doubling and star steps may be implemented in the addition chain. This approach eliminates the computationally expensive step of generating the addition chain from an exponent, and therefore can greatly reduce the computation time of the modular exponentiation.

Abstract: A database has a plurality of entries and a plurality of attributes common to each entry, where each entry corresponds to an individual. A query q is received from a querying entity query q and is passed to the database, and an answer a is received in response. An amount of noise e is generated and added to the answer a to result in an obscured answer o, and the obscured answer o is returned to the querying entity. Thus, a level of protection of privacy is provided to each individual represented within the database.

Abstract: A database has a plurality of entries and a plurality of attributes common to each entry, where each entry corresponds to an individual. A query is received from a querying entity query and is passed to the database, and an answer is received in response. An amount of noise is generated and added to the answer to result in an obscured answer, and the obscured answer is returned to the querying entity. The noise is normally distributed around zero with a particular variance. The variance R may be determined in accordance with R>8 T log2(T/?)/?2, where T is the permitted number of queries T, ? is the utter failure probability, and ? is the largest admissible increase in confidence. Thus, a level of protection of privacy is provided to each individual represented within the database. Example noise generation techniques, systems, and methods may be used for privacy preservation in such areas as k means, principal component analysis, statistical query learning models, and perceptron algorithms.

Abstract: An addition chain is first generated, and then an integer x is derived from it. Doubling and star steps may be implemented in the addition chain. This approach eliminates the computationally expensive step of generating the addition chain from an exponent, and therefore can greatly reduce the computation time of the modular exponentiation.

Abstract: A memory containing an authenticated search tree that serves for authenticating membership or non membership of items in a set. The authenticated search tree including a search tree having nodes and leaves and being associated with a search scheme. The nodes including dynamic search values and the leaves including items of the set. The nodes are associated, each, with a cryptographic hash function value that is produced by applying a cryptographic hash function to the cryptographic hash values of the children nodes and to the dynamic search value of the node. The root node of the authenticated search tree is authenticated by a digital signature.