A framework for fair classification comprising a (hypothetical) task-specific metric for determining the degree to which individuals are similar with respect to the classification task at hand and an algorithm for maximizing utility subject to the fairness constraint, that similar individuals are treated similarly is presented.
A deterministic, log-space algorithm that solves st-connectivity in undirected graphs and implies a way to construct in log- space a fixed sequence of directions that guides a deterministic walk through all of the vertices of any connected graph.
The pseudo-randomness of one construction is proved under the assumption that Blum integers is hard while the other construction is pseudo- random if the decisional version of the Diffie--Hellman assumption holds.
The first one-round (two-pass) protocol for oblivious transfer that does not rely on the random oracle model is presented, which is a special case of a more general "conditional disclosure" methodology, which extends a previous approach from [11] and adapts it to the 2-party setting.
A new construction of pseudo-random functions such that computing their value at any given point involves two multiple products, much more efficient than previous proposals.
It is shown that, surprisingly, there is a way to estimate an exponential in n number of expectations accurately even if the functions are chosen adaptively, and this gives an exponential improvement over standard empirical estimators that are limited to a linear number of estimates.
This work investigates the problem of privacy-preserving access to a database, where records in the database are accessed according to their associated keywords and gives efficient solutions for various settings of KS.
A deterministic, log-space algorithm that solves st-connectivity in undirected graphs and implies a way to construct in log- space a fixed sequence of directions that guides a deterministic walk through all of the vertices of any connected graph.
The solutions presented enable the operation of many servers, preventing bottlenecks or single points of failure and are far better than the known partitioning to domains or replication solutions to this problem, and are especially suited to handle users of multicast groups.
A simple and practical method for reusing a holdout set to validate the accuracy of hypotheses produced by a learning algorithm operating on a training set and it is shown that a simple approach based on description length can also be used to give guarantees of statistical validity in adaptive settings.