k-Nearest Neighbor Classification over Semantically Secure Encrypted Relational Data (1403.5001v3)

Abstract: Data Mining has wide applications in many areas such as banking, medicine, scientific research and among government agencies. Classification is one of the commonly used tasks in data mining applications. For the past decade, due to the rise of various privacy issues, many theoretical and practical solutions to the classification problem have been proposed under different security models. However, with the recent popularity of cloud computing, users now have the opportunity to outsource their data, in encrypted form, as well as the data mining tasks to the cloud. Since the data on the cloud is in encrypted form, existing privacy preserving classification techniques are not applicable. In this paper, we focus on solving the classification problem over encrypted data. In particular, we propose a secure k-NN classifier over encrypted data in the cloud. The proposed k-NN protocol protects the confidentiality of the data, user's input query, and data access patterns. To the best of our knowledge, our work is the first to develop a secure k-NN classifier over encrypted data under the semi-honest model. Also, we empirically analyze the efficiency of our solution through various experiments.

• The paper introduces a secure k-NN protocol that preserves data privacy, query inputs, and access patterns in cloud-based encrypted databases.
• The protocol utilizes secure sub-protocols including SSED, SBD, SMIN, and SF to compute distances and determine the majority class without exposing sensitive data.
• Empirical evaluations show scalable performance with moderate computational costs, highlighting its practical feasibility for privacy-preserving data mining.

Secure $k$-Nearest Neighbor Classification Over Encrypted Data

This paper addresses the rising demand for privacy-preserving data mining in the context of outsourced databases, particularly within cloud computing environments. As cloud computing enables users to outsource not only their data but also data-processing tasks, ensuring the confidentiality and security of sensitive information becomes paramount. This paper focuses on the challenge of performing classification tasks on encrypted data, introducing a secure $k$-Nearest Neighbor (PP$k$NN) classifier under the semi-honest model.

Key Contributions

The central contribution of this paper is the design of a protocol for executing $k$-Nearest Neighbor (k-NN) classification over encrypted databases stored in the cloud while preserving data privacy. The authors propose a PP$k$NN protocol that safeguards the security of the data, query inputs by users, and access patterns from any participating entities, including the cloud service provider. This protocol is notable for being the first to operate under the standard semi-honest model, which assumes parties follow protocol specifications but might attempt to glean additional insights from observed interactions.

Protocol Overview

The protocol operates in two main stages:

1. Secure Retrieval of $k$-Nearest Neighbors (SR$k$NN): This stage involves calculating the distances between the query and each encrypted data point without exposing any plaintext information. The system identifies the $k$-nearest neighbors through several secure sub-protocols, including Secure Squared Euclidean Distance (SSED), Secure Bit-Decomposition (SBD), and Secure Minimum out of $n$ Numbers (SMIN$_n$).
2. Secure Computation of Majority Class (SCMC$_k$): Once the $k$-nearest neighbors are determined, this stage computes the most frequent class label among them using a Secure Frequency (SF) protocol and a secure maximum (SMAX$_w$) operation to identify the majority class label.

Efficiency and Security

The authors comprehensively evaluate the computational efficiency of the protocol compared to possible alternatives. Their solution emphasizes the practical feasibility of the protocol by demonstrating moderate computational costs and feasible communication overheads. Moreover, the protocol effectively balances security requirements with computational efficiency, an essential aspect given the increasing practicality of privacy-preserving data mining systems.

The security of the proposed PP$k$NN protocol is rigorously analyzed under the semi-honest model using simulation-based proofs. These proofs validate that no additional information beyond the specified output is revealed to any participating entity during the protocol's execution. Furthermore, the authors discuss extension strategies for achieving security in a malicious adversary setting, making the protocol more robust and applicable to a wider range of potential cloud-based applications.

Empirical Analysis and Implications

Empirical evaluations on real datasets demonstrate the protocol’s scalability, with computation costs scaling linearly with the number of data points and $k$-nearest neighbors chosen. These results highlight the protocol's applicability to realistic datasets and scenarios, establishing a benchmark for secure outsourced computation without compromising data utility.

Given the current trend towards cloud-based applications, these advancements in privacy-preserving techniques hold significant practical implications. They enable enterprises and individuals to leverage cloud computing capabilities while maintaining strict confidentiality of sensitive data. This holds promising future developments, especially with increasing regulatory demands for data protection in various sectors including finance, healthcare, and governmental operations.

Conclusion

The work presented in this paper provides a fundamental step towards efficient and secure deployment of $k$-NN classification systems in encrypted, outsourced database environments. Such protocols are crucial for enabling privacy-conscious applications of machine learning and AI technologies in the growing field of cloud computing services. Continued research in optimizing these privacy-preserving protocols, including their adaptation to other classification models beyond $k$-NN, remains a fertile area for academic and practical exploration.