Integrity Verification for Outsourcing Uncertain Frequent Itemset Mining

TL;DR

This paper addresses the challenge of verifying the correctness of uncertain frequent itemset mining results outsourced to third-party providers, extending existing verification methods to uncertain data scenarios with probabilistic guarantees.

Contribution

It extends deterministic outsourcing verification techniques to uncertain data, introduces improved schemes for different UFI definitions, and discusses probabilistic correctness guarantees.

Findings

Enhanced verification schemes for uncertain frequent itemsets.

Probabilistic guarantees for approximate UFI mining results.

Comparative analysis demonstrating scheme effectiveness.

Abstract

In recent years, due to the wide applications of uncertain data (e.g., noisy data), uncertain frequent itemsets (UFI) mining over uncertain databases has attracted much attention, which differs from the corresponding deterministic problem from the generalized definition and resolutions. As the most costly task in association rule mining process, it has been shown that outsourcing this task to a service provider (e.g.,the third cloud party) brings several benefits to the data owner such as cost relief and a less commitment to storage and computational resources. However, the correctness integrity of mining results can be corrupted if the service provider is with random fault or not honest (e.g., lazy, malicious, etc). Therefore, in this paper, we focus on the integrity and verification issue in UFI mining problem during outsourcing process, i.e., how the data owner verifies the mining results. Specifically, we explore and extend the existing work on deterministic FI outsourcing verification to uncertain scenario. For this purpose, We extend the existing outsourcing FI mining work to uncertain area w.r.t. the two popular UFI definition criteria and the approximate UFI mining methods. Specifically, We construct and improve the basic/enhanced verification scheme with such different UFI definition respectively. After that, we further discuss the scenario of existing approximation UFP mining, where we can see that our technique can provide good probabilistic guarantees about the correctness of the verification. Finally, we present the comparisons and analysis on the schemes proposed in this paper.