Titlebook: Algorithms for Data and Computation Privacy; Alex X. Liu,Rui Li Book 2021 The Editor(s) (if applicable) and The Author(s), under exclusive

LIKEN

泛濫

Predictable Privacy-Preserving Mobile Crowd Sensinging mobile crowd sensing, which we envision to have the capability to quantify the privacy protections, while also allowing application users to predict the utility loss at the same time. The . algorithm is first proposed to protect the private data against data reconstruction attacks. To understand

hypnotic

策略

arboretum

Range Queries over Encrypted Datacheme is secure under the widely adopted IND-CKA security model. We propose two algorithms, namely PBtree traversal width minimization and PBtree traversal depth minimization, to improve query processing efficiency. We prove that the worst case complexity of our query processing algorithm using PBtr

Heart-Attack

Interregnum

細(xì)絲

Top-k Queries for Two-Tiered Sensor Networksitem into an interval and attach the partition information with the data. The attached information ensures that the sink can verify the integrity of query results. We formally prove that our scheme is secure under IND-CKA security model. Our experimental results on real-life data show that our appro

殺死

Alopecia-Areata

Privacy Preserving Quantification of Cross-Domain Network Reachabilityivacy of the individual ACLs. This protocol can accurately determine the network reachability along a network path through different administrative domains. We have implemented and evaluated our protocol on both real and synthetic ACLs. The experimental results show that the on-line processing time