Preserving Confidentiality in Data Analytics-as-a-Service

The enhancements in computation technologies in the last decades enabled businesses to analyze the data that is collected through their systems which helps to improve their services.
However, performing data analytics remains a challenging task for small- and medium-scale companies due to the lack of in-house experience and computational resources.
Data Analytics-as-a-Service (DAaaS) paradigm provides such companies outsourced data analytics, where a company that is specialized in data analytics serves its knowledge and computational resources to the other companies, which need data analytics for their businesses.

A major challenge in DAaaS is preserving the privacy of the outsourced data, which might contain sensitive customer or employee information or the intellectual property of the outsourcing company. Leakage of sensitive information has several consequences both for outsourcing and service provider companies as legal obligations, loss of reputation, and financial loss. Therefore, a well functioning outsourced analytics service should achieve several data protection measures such as confidentiality, integrity, and availability.

In this thesis, we focus on the preservation of confidentiality in data analytics-as-a-service applications. We select three analytics applications that are becoming popular in outsourced data analytics, which are process analytics, machine learning, and marketing analytics. Despite there exist several other techniques that are commonly used in outsourced data analytics, we decide to focus on the algorithms of process analytics, machine learning, and marketing analytics since the privacy concerns in these analytics have not been investigated thoroughly.

In confidential data analytics-as-a-service, our goal is to achieve confidentiality by protecting input/output privacy and maintaining the correctness and efficiency of analytics computations. To protect the privacy of data we use two secure computation techniques, which are homomorphic encryption and secure multiparty computation. To assure correctness, we propose several hybrid protocol designs that minimize the loss of accuracy in computations. For the efficiency of our protocols, we use several optimization techniques that reduce the computation and communication costs of private data analytics. Our protocols show promising results for confidential data analytics in the outsourced setting.