15,104 research outputs found
The Complexity of Power-Index Comparison
We study the complexity of the following problem: Given two weighted voting
games G' and G'' that each contain a player p, in which of these games is p's
power index value higher? We study this problem with respect to both the
Shapley-Shubik power index [SS54] and the Banzhaf power index [Ban65,DS79]. Our
main result is that for both of these power indices the problem is complete for
probabilistic polynomial time (i.e., is PP-complete). We apply our results to
partially resolve some recently proposed problems regarding the complexity of
weighted voting games. We also study the complexity of the raw Shapley-Shubik
power index. Deng and Papadimitriou [DP94] showed that the raw Shapley-Shubik
power index is #P-metric-complete. We strengthen this by showing that the raw
Shapley-Shubik power index is many-one complete for #P. And our strengthening
cannot possibly be further improved to parsimonious completeness, since we
observe that, in contrast with the raw Banzhaf power index, the raw
Shapley-Shubik power index is not #P-parsimonious-complete.Comment: 12 page
Order-Revealing Encryption and the Hardness of Private Learning
An order-revealing encryption scheme gives a public procedure by which two
ciphertexts can be compared to reveal the ordering of their underlying
plaintexts. We show how to use order-revealing encryption to separate
computationally efficient PAC learning from efficient -differentially private PAC learning. That is, we construct a concept
class that is efficiently PAC learnable, but for which every efficient learner
fails to be differentially private. This answers a question of Kasiviswanathan
et al. (FOCS '08, SIAM J. Comput. '11).
To prove our result, we give a generic transformation from an order-revealing
encryption scheme into one with strongly correct comparison, which enables the
consistent comparison of ciphertexts that are not obtained as the valid
encryption of any message. We believe this construction may be of independent
interest.Comment: 28 page
High-Multiplicity Fair Allocation Using Parametric Integer Linear Programming
Using insights from parametric integer linear programming, we significantly
improve on our previous work [Proc. ACM EC 2019] on high-multiplicity fair
allocation. Therein, answering an open question from previous work, we proved
that the problem of finding envy-free Pareto-efficient allocations of
indivisible items is fixed-parameter tractable with respect to the combined
parameter "number of agents" plus "number of item types." Our central
improvement, compared to this result, is to break the condition that the
corresponding utility and multiplicity values have to be encoded in unary
required there. Concretely, we show that, while preserving fixed-parameter
tractability, these values can be encoded in binary, thus greatly expanding the
range of feasible values.Comment: 15 pages; Published in the Proceedings of ECAI-202
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