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R-PEKS: RBAC Enabled PEKS for Secure Access of Cloud Data
In the recent past, few works have been done by combining attribute-based access control with multi-user PEKS, i.e., public key encryption with keyword search. Such attribute enabled searchable encryption is most suitable for applications where the changing of privileges is done once in a while. However, to date, no efficient and secure scheme is available in the literature that is suitable for these applications where changing privileges are done frequently. In this paper our contributions are twofold. Firstly, we propose a new PEKS scheme for string search, which, unlike the previous constructions, is free from bi-linear mapping and is efficient by 97% compared to PEKS for string search proposed by Ray et.al in TrustCom 2017. Secondly, we introduce role based access control (RBAC) to multi-user PEKS, where an arbitrary group of users can search and access the encrypted files depending upon roles. We termed this integrated scheme as R-PEKS. The efficiency of R-PEKS over the PEKS scheme is up to 90%. We provide formal security proofs for the different components of R-PEKS and validate these schemes using a commercial dataset
How to Price Shared Optimizations in the Cloud
Data-management-as-a-service systems are increasingly being used in
collaborative settings, where multiple users access common datasets. Cloud
providers have the choice to implement various optimizations, such as indexing
or materialized views, to accelerate queries over these datasets. Each
optimization carries a cost and may benefit multiple users. This creates a
major challenge: how to select which optimizations to perform and how to share
their cost among users. The problem is especially challenging when users are
selfish and will only report their true values for different optimizations if
doing so maximizes their utility. In this paper, we present a new approach for
selecting and pricing shared optimizations by using Mechanism Design. We first
show how to apply the Shapley Value Mechanism to the simple case of selecting
and pricing additive optimizations, assuming an offline game where all users
access the service for the same time-period. Second, we extend the approach to
online scenarios where users come and go. Finally, we consider the case of
substitutive optimizations. We show analytically that our mechanisms induce
truth- fulness and recover the optimization costs. We also show experimentally
that our mechanisms yield higher utility than the state-of-the-art approach
based on regret accumulation.Comment: VLDB201
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