Privacy-Aware Data Cleaning-as-a-Service (Extended Version)

TL;DR

This paper introduces PACAS, a privacy-aware data cleaning model in a cloud setting that balances data utility and sensitivity protection through innovative data repairs, semantic distance measures, and an extended anonymity framework.

Contribution

The paper presents PACAS, a novel privacy-aware data cleaning framework with generalized data repairs, a semantic utility measure, and an extended anonymity model for sensitive data sharing.

Findings

PACAS effectively safeguards sensitive data while maintaining data utility.

It achieves lower repair errors compared to existing privacy-aware cleaning methods.

The model supports semantic considerations in data privacy and cleaning processes.

Abstract

Data cleaning is a pervasive problem for organizations as they try to reap value from their data. Recent advances in networking and cloud computing technology have fueled a new computing paradigm called Database-as-a-Service, where data management tasks are outsourced to large service providers. In this paper, we consider a Data Cleaning-as-a-Service model that allows a client to interact with a data cleaning provider who hosts curated, and sensitive data. We present PACAS: a Privacy-Aware data Cleaning-As-a-Service model that facilitates interaction between the parties with client query requests for data, and a service provider using a data pricing scheme that computes prices according to data sensitivity. We propose new extensions to the model to define generalized data repairs that obfuscate sensitive data to allow data sharing between the client and service provider. We present a new semantic distance measure to quantify the utility of such repairs, and we re-define the notion of consistency in the presence of generalized values. The PACAS model uses (X,Y,L)-anonymity that extends existing data publishing techniques to consider the semantics in the data while protecting sensitive values. Our evaluation over real data show that PACAS safeguards semantically related sensitive values, and provides lower repair errors compared to existing privacy-aware cleaning techniques.