Privacy-preserving K-nearest Neighbor Research Articles

Privacy-Preserving k-Nearest Neighbor Classification over Malicious Participants in Outsourced Cloud Environments

In recent years, many companies have chosen to outsource data and other data computation tasks to cloud service providers to reduce costs and increase efficiency. However, there are risks of security and privacy breaches when users outsource data to a cloud environment. Many researchers have proposed schemes based on cryptographic primitives to address these risks under the assumption that the cloud is a semi-honest participant and query users are honest participants. However, in a real-world environment, users’ data privacy and security may be threatened by the presence of malicious participants. Therefore, a novel scheme based on secure multi-party computation is proposed when attackers gain control over both the cloud and a query user in the paper. We prove that our solution can satisfy our goals of security and privacy protection. In addition, our experimental results based on simulated data show feasibility and reliability.

Parallelly Running and Privacy-Preserving k-Nearest Neighbor Classification in Outsourced Cloud Computing Environments

Classification is used in various areas where k-nearest neighbor classification is the most popular as it produces efficient results. Cloud computing with powerful resources is one reliable option for handling large-scale data efficiently, but many companies are reluctant to outsource data due to privacy concerns. This paper aims to implement a privacy-preserving k-nearest neighbor classification (PkNC) in an outsourced environment. Existing work proposed a secure protocol (SkLE/SkSE) to compute k data with the largest/smallest value privately, but this work discloses information. Moreover, SkLE/SkSE requires a secure comparison protocol, and the existing protocols also contain information disclosure problems. In this paper, we propose a new secure comparison and SkLE/SkSE protocols to solve the abovementioned information disclosure problems and implement PkNC with these novel protocols. Our proposed protocols disclose no information and we prove the security formally. Then, through extensive experiments, we demonstrate that the PkNC applying the proposed protocols is also efficient. Especially, the PkNC is suitable for big data analysis to handle large amounts of data, since our SkLE/SkSE is executed for each dataset in parallel. Although the proposed protocols do require efficiency sacrifices to improve security, the running time of our PkNC is still significantly more efficient compared with previously proposed PkNCs.

Query on the cloud: improved privacy-preserving k-nearest neighbor classification over the outsourced database

Query on the cloud: improved privacy-preserving k-nearest neighbor classification over the outsourced database

Privacy-preserving spatial query protocol based on the Moore curve for location-based service

The cloud computing has attracted a multitude of data owners to outsource computing and acquire location-based services from cloud service providers. The privacy-preserving spatial query on outsourced data has become a research hotspot in the location-based service. The existing schemes may damage the confidentiality of data, or be trapped by the large computation and communication overhead. The majority of them focus on the protection of original data and location information, while ignoring the significance of query records. In order to find a balance between security, efficiency and accuracy, this paper proposes a spatial transformation scheme for location-based services in outsourced environment, and designs a privacy-preserving k-nearest neighbor (k-NN) protocol. We utilize the Moore curve to perform a one-way transformation of the original data, and adopt the encryption to prevent malicious access to the transformed data by untrusted entities. The proposed protocol accomplishes efficient and accurate k-NN query in the transformed space while guaranteeing the confidentiality of outsourced data and the user's location. In addition, we propose a secure optimization method based on the oblivious transfer to protect the privacy of query records. Performance analysis shows that the proposed protocol has considerable efficiency advantage over existing schemes without sacrificing security and accuracy.

A Privacy Preserving Cloud-Based K-NN Search Scheme with Lightweight User Loads

With the growing popularity of cloud computing, it is convenient for data owners to outsource their data to a cloud server. By utilizing the massive storage and computational resources in cloud, data owners can also provide a platform for users to make query requests. However, due to the privacy concerns, sensitive data should be encrypted before outsourcing. In this work, a novel privacy preserving K-nearest neighbor (K-NN) search scheme over the encrypted outsourced cloud dataset is proposed. The problem is about letting the cloud server find K nearest points with respect to an encrypted query on the encrypted dataset, which was outsourced by data owners, and return the searched results to the querying user. Comparing with other existing methods, our approach leverages the resources of the cloud more by shifting most of the required computational loads, from data owners and query users, to the cloud server. In addition, there is no need for data owners to share their secret key with others. In a nutshell, in the proposed scheme, data points and user queries are encrypted attribute-wise and the entire search algorithm is performed in the encrypted domain; therefore, our approach not only preserves the data privacy and query privacy but also hides the data access pattern from the cloud server. Moreover, by using a tree structure, the proposed scheme could accomplish query requests in sub-liner time, according to our performance analysis. Finally, experimental results demonstrate the practicability and the efficiency of our method.

Toward Highly Secure Yet Efficient KNN Classification Scheme on Outsourced Cloud Data

Nowadays, outsourcing data and machine learning tasks, e.g., $k$ -nearest neighbor (KNN) classification, to clouds has become a scalable and cost-effective way for large scale data storage, management, and processing. However, data security and privacy issue have been a serious concern in outsourcing data to clouds. In this article, we propose a privacy-preserving KNN classification scheme on cloud data in a twin-cloud model based on an additively homomorphic cryptosystem and secret sharing. Compared with existing works, we redesign a set of lightweight building blocks, such as secure square Euclidean distance, secure comparison, secure sorting, secure minimum, and maximum number finding, and secure frequency calculating, which achieve the same security level but with higher efficiency. In our scheme, data owners stay offline, which is different from secure-multiparty computation-based solutions which require data owners’ stay online during computation. In addition, query users do not interact with the cloud except sending query data and receiving the query results. Our security analysis shows that the scheme protects outsourced data security and query privacy, and hides access patterns. The experiments on real-world dataset indicate that our scheme is significantly more efficient than existing schemes.

Efficient k-Nearest Neighbor Classification Over Semantically Secure Hybrid Encrypted Cloud Database

Nowadays, individuals and companies increasingly tend to outsource their databases and further data operations to cloud service provides. However, utilizing the cost-saving advantages of cloud computing brings about the risk of violating database security and user’s privacy. In this paper, we focus on the problem of privacy-preserving k-nearest neighbor (kNN) classification, in which a query user (QU) submits an encrypted query point to a cloud server (CS) and asks for the kNN classification labels based on the encrypted cloud database outsourced by a data owner (DO), without disclosing any privacy of DO or QU to CS. Previous secure kNN query schemes either cannot fully achieve required security properties or introduce heavy computation costs, making them not practical in real-world applications. To better solve this problem, we propose a novel efficient privacy-preserving kNN classification protocol over semantically secure hybrid encrypted cloud database using Paillier and ElGamal cryptosystems. The proposed protocol protects both database security and query privacy and also hides data access patterns from CS. We formally analyze the security of our protocol and evaluate the performance through extensive experiments. The experiment results show that the computation cost of our protocol is about two orders of magnitude lower than that of the state-of-the-art protocol while achieving the same security and privacy properties.

Privacy-Preserving k-Nearest Neighbor Computation in Multiple Cloud Environments

With the advent of big data era, clients lack of computational and storage resources tends to outsource data mining tasks to cloud computing providers in order to improve efficiency and save costs. Generally, different clients choose different cloud companies for the sake of security, business cooperation, location, and so on. However, due to the rise of privacy leakage issues, the data contributed by clients should be encrypted under their own keys. This paper focuses on privacy-preserving k-nearest neighbor (kNN) computation over the databases distributed among multiple cloud environments. Unfortunately, existing secure outsourcing protocols are either restricted to a single key setting or quite inefficient because of frequent client-to-server interactions, making it impractical for wide application. To address these issues, we propose a set of secure building blocks and outsourced collaborative kNN protocol. Theoretical analysis shows that our scheme not only preserves the privacy of distributed databases and kNN query but also hides access patterns in the semi-honest model. Experimental evaluation demonstrates its significant efficiency improvements compared with existing methods.