Authenticating Aggregate Queries over Set-Valued Data with Confidentiality

Abstract

With recent advances in data-as-a-service (DaaS) and cloud computing, aggregate query services over set-valued data are becoming widely available for business intelligence that drives decision making. However, as the service provider is often a third-party delegate of the data owner, the integrity of the query results cannot be guaranteed and is thus imperative to be authenticated. Unfortunately, existing query authentication techniques either do not work for set-valued data or they lack data confidentiality. In this paper, we propose authenticated aggregate queries over set-valued data that not only ensure the integrity of query results but also preserve the confidentiality of source data. As many aggregate queries are composed of multiset operations such as set union and subset, we first develop a family of privacy-preserving authentication protocols for primitive multiset operations. Using these protocols as building blocks, we present a privacy-preserving authentication framework for various aggregate queries and further optimize their authentication performance. Security analysis and empirical evaluation show that our proposed privacy-preserving authentication techniques are feasible and robust under a wide range of system workloads.

Cloud based multicasting using fat tree data confidential recurrent neural network

“…This section analyzes the impact of the proposed FT-DCRNN technique via Cloud Sim simulator. The cloudsim goal is to provide a global and extensible simulation framework that compares the proposed FT-DCRNN technique with the existing PAPA [1] and MVNE [2]. The experiment is conducted to measure and evaluate the FT-DCRNN technique on the factors such as throughput, efficiency, average response time and DC.…”

“…Throughput and efficiency of FT-DCRNN technique is evaluated with PAPA [1] and MVNE [2] using the number of user requests as input in the cloud environment in the comparison experiments. The two imperative factors are considered for evaluating infrastructure services afforded by the cloud environment are Throughput and Efficiency.…”

“…T = i Exe time (i, j) + OH time (11) From the above equation (11), the throughput value "T" is measured based on the execution time "Exe time " and the overhead time "OH time ". Table 1 given below provides the throughput and efficiency distribution of all the three techniques, FT-DCRNN, PAPA [1] and MVNE [2]. This experiment is set to verify the high effectiveness of the throughput against the CU requests placed in a cloud environment.…”

“…However, CSP is frequently a third-party delegate of the data owner, results of query related to integrity is not guaranteed and hence indispensable be authenticated. Privacy-Preserving Authentication framework for Aggregate (PAPA) [1] queries intended authenticated aggregate queries. PAPA affords integrity of query results and preserves the DC.…”

Distributed kNN Query Authentication

“…Chen et al [7] developed a novel top-k query authentication based on the cryptography building blocks. Xu et al [11] considered both authenticity and confidentiality for aggregate queries over set-valued data. Lin et al [8] proposed a new ADS called MR-Sky-tree to facilitate the location-based authenticated skyline queries.…”