Title: Confusion: A confidential fusion approach to statistical secret sharing
Authors: Murray Pollock - University of Warwick (United Kingdom) [presenting]
Louis Aslett - Durham University (United Kingdom)
Hongsheng Dai - University of Essex (United Kingdom)
Gareth Roberts - University of Warwick (United Kingdom)
Abstract: A surprisingly challenging problem in computational statistics is how to unify distributed statistical analyses and inferences into a single coherent inference. This problem arises in many settings (for instance, combining experts in expert elicitation, incorporating disparate inference in multi-view learning, and recombining in distributed big data problems), but a general framework for conducting such unification has only recently been addressed. A particularly compelling application is in statistical cryptography. Consider the setting in which multiple (potentially untrusted) parties wish to share distributional information (for instance in insurance, banking and social media settings), but wish to ensure information theoretic security (in particular, information is shared in such a way that another party with unbounded compute power could not determine secret information or data of any other party). Confusion, a confidential fusion approach to statistical secret sharing, is the first approach which addresses this important statistical application.