Encrypted Fast Covariance Intersection Without Leaking Fusion Weights

Abstract

State estimate fusion is a common requirement in distributed sensor networks and can be complicated by untrusted participants or network eavesdroppers. We present a method for computing the common Fast Covariance Intersection fusion algorithm on an untrusted cloud without disclosing individual estimates or the fused result. In an existing solution to this problem, fusion weights corresponding to estimate errors are leaked to the cloud to perform the fusion. In this work, we present a method that guarantees no data identifying estimators or their estimated values is leaked to the cloud by requiring an additional computation step by the party querying the cloud for the fused result. The Paillier encryption scheme is used to homomorphically compute separate parts of the computation that can be combined after decryption. This encrypted Fast Covariance Intersection algorithm can be used in scenarios where the fusing cloud is not trusted and any information on estimator performances must remain confidential.