Abstract: A system and a method for allocating indivisible resources to agents over a finite horizon in a manner that balances resource utilization and fairness are provided. The method includes: receiving first information that relates to a number of agents included in a set of agents; receiving second information that relates to whether each respective agent is requesting an allocation of an indivisible resource during a particular allocation round; assigning, to each respective agent, a respective weight that relates to an importance of the respective agent; calculating, for each respective agent, a respective agent-specific allocation probability; and determining an allocation of the indivisible resource for the particular allocation round based on the agent-specific allocation probabilities. The determination of the allocation may be adjusted based on a budget that indicates an availability of the resource during the particular allocation round.

Abstract: A method for preserving privacy with respect to modeling event sequence data is provided. The method includes: receiving information about a sequence of events; modeling the event sequence by a Hawkes process that has an intensity that includes an exogenous base intensity rate and an indigenous component that has an excitation rate and a decay rate; analyzing the received information; and determining estimated values of the exogenous base intensity rate and the excitation rate, such that an accuracy of the estimates corresponds to a length of time over which the sequence of events is observed. Differential privacy is introduced by adding noise to the sequence of events in order to preserve the privacy of individuals associated with the events, and a cost of the differential privacy is expressible as an additional length of observation time required to ensure the accuracy of the estimates.

Abstract: Systems and methods for generation of a synthetic dataset that simultaneously represents the original data and preserves privacy are provided. The objective of answering statistical queries in a differentially-private manner is addressed by providing data-independent and data-dependent algorithms based on space partitioning for differentially private dataset release. These algorithms are intuitive and transparent, resulting in theoretical results on the utility-privacy tradeoffs where utility is measured with respect to kernel density preservation. The data-dependent approach overcomes the curse of dimensionality and leads to a scalable algorithm.

Abstract: A system and a method for allocating limited resources to agents that reveal their stochastic demands on arrival over a finite horizon in a proportionally fair manner that exhausts available resource budgets are provided. The method includes: receiving, from an entity, a request for a resource for which a predetermined maximum amount of the resource is available for allocation; estimating a number of future requests and amounts of the resource to be requested within a predetermined time interval; adjusting a result of the estimating by calculating a standard deviation of an uncertainty of at least one future request; and determining, based on the estimates and the adjustment, an amount of the resource to allocated to the entity by applying an algorithm with respect to the adjusted estimates and the predetermined maximum available amount of the resources.

Abstract: A method for preserving privacy with respect to modeling event sequence data is provided. The method includes: receiving information about a sequence of events; modeling the event sequence by a Hawkes process that has an intensity that includes an exogenous base intensity rate and an indigenous component that has an excitation rate and a decay rate; analyzing the received information; and determining estimated values of the exogenous base intensity rate and the excitation rate, such that an accuracy of the estimates corresponds to a length of time over which the sequence of events is observed. Differential privacy is introduced by adding noise to the sequence of events in order to preserve the privacy of individuals associated with the events, and a cost of the differential privacy is expressible as an additional length of observation time required to ensure the accuracy of the estimates.