Secure kNN Research Articles

Privacy-preserving verifiable fuzzy phrase search over cloud-based data

Today, cloud computing plays an increasingly important role in cloud-based data processing systems. Privacy-preserving phrase search is one critical search technique in cloud-based data processing systems, which allows for the retrieval of cipher documents containing a set of consecutive keywords. However, the existing privacy-preserving phrase search schemes rarely support fault tolerance and result verification at the same time. To deal with these problems, this paper presents a Privacy-Preserving Verifiable Fuzzy Phrase Search scheme over cloud-based data (PPVFPS). We construct a novel keyword matching list by the keyword transformation techniques and the secure kNN algorithm to support fuzzy search. To enhance search efficiency and achieve the dynamic update, we generate a counting Bloom filter based on the virtual binary tree, which can help find the documents containing the search keywords. In order to securely judge the position relationship between two search keywords, we employ the techniques of homomorphic encryption and bilinear mapping to encrypt the positions of the keywords. We generate the verification tags based on the MAC technique and build a document index table to realize result verification. We demonstrate the security analysis of PPVFPS scheme, and the experimental result shows that PPVFPS scheme can achieve high accuracy.

Towards efficient Secure Boolean Range Query over encrypted spatial data

In recent years, the development of mobile application services and geographic positioning technology has promoted the rapid growth of spatio-textual data. Boolean Range Query (BRQ) can return all objects containing all query keywords within the query range. In order to save the overhead of storage and management, users usually encrypt their spatio-textual data and outsource them to the cloud server. We focus on how to realize efficient secure boolean range query over encrypted spatio-textual data, and propose a Secure Boolean Range Query scheme (SBRQ) in this paper. To realize efficient range query over high-dimensional space, Hilbert curve is employed to reduce the dimension of space. The secure index based on the Quadtree structure is built to further improve the query efficiency, which avoids scanning the whole database, and therefore achieves sub-linear query complexity. The secure KNN algorithm is adopted to ensure data security. The security of SBRQ is analyzed from the index security and the token security, which shows that SBRQ is IND-SCPA secure with different leakage functions. Based on a series of experiments, SBRQ provides better query efficiency than other typical schemes.

Secure kNN query of outsourced spatial data using two-cloud architecture

Secure kNN query of outsourced spatial data using two-cloud architecture

Efficient secure and verifiable KNN set similarity search over outsourced clouds

Efficient secure and verifiable KNN set similarity search over outsourced clouds

Efficient Privacy-Preserving Geographic Keyword Boolean Range Query Over Encrypted Spatial Data

With the widespread popularity of mobile devices and geolocation-related services, spatial keyword data has exploded in recent years. As an application, people are accustomed to using specific keywords to search for data in a given geometric range. To protect user privacy, searchable encryption technologies are used to encrypt data and user queries. Most existing works focus on either spatial attributes or keyword attributes over encrypted spatial keyword data, which cannot solve the problem of geographic keyword range queries directly. And several other works considering these two attributes have some limitations in terms of query efficiency and security assurance. In this article, we propose an efficient privacy-preserving geographic keyword Boolean range query (EPBRQ) scheme to solve existing challenges in the current work. In particular, we design a recoding algorithm to break the limits of the current work to achieve lower time complexity and employ secure Knn computation to protect user data privacy comprehensively. The security analysis shows that our solution can well protect the privacy of data and queries from cloud server threats. And numerous experiments based on real-world data also show that our scheme provides better query efficiency than existing works.

Secure and Efficient Semantic Extension Search over Encrypted Documents by Integrating Cloud and Fog Systems

With the explosive increase of document files, more and more data owners outsource their documents to the public cloud, which can decrease the costs of local data management systems. However, the problem of information privacy leakage in the cloud is a great challenge and it has been attracting more and more attention. In this article, we propose a secure and efficient document search scheme, named SES, based on both the cloud and fog systems. All the documents are symmetrically encrypted before being outsourced to the cloud, and an index vector is constructed based on the keywords for each document. Specifically, we integrate the position information of keywords into the TF-IDF model to generate document vectors, which are accurate and inherent summarizations about the documents. In query requests, a data user needs to provide a set of keywords, which are first extended by the Word2Vec tool and then mapped to a query vector. The extension process of keywords makes the provided keywords more comprehensive and accurate, and hence, it improves document search accuracy. To achieve the forward and backward security, both the document vectors and query vectors are appended with an ingenious vector. The relevance score between a document and a query is defined as the inner product of the document vector and the query vector. We return the most k relevant documents as the search results to the data users. To protect the contextual information stored in the document and query vectors, we encrypt the vectors by the secure kNN algorithm. To improve the search efficiency, a searchable index structure for the document set is constructed based on the Diffie–Hellman secret key negotiation algorithm. The analysis and simulation results illustrate that the proposed scheme performs well in terms of both security and search efficiency.

A privacy preserving data aggregation and query for metro passenger flow via mobile crowdsensing

SummaryWith the popularity of mobile Internet networks and mobile terminal equipment, mobile crowdsensing (MCS) has become a popular and economical way of data collection, sharing, and query services. However, the existing privacy‐preserving query schemes MCS‐based less consider the sensing user's privacy leakage at data aggregation phase and querier's privacy leakage at query service phase simultaneously. In this article, we propose a novel privacy‐preserving aggregation and query scheme applied in metro passenger flow via MCS, which achieves the metro passenger flow acquisition and query service and protects the sensing user's identity and location privacy as well as the querier's query privacy. We exploit Paillier cryptosystem and pseudonym mechanism to prevent the privacy leakage, and utilize secure kNN algorithm to protect the query privacy. Meanwhile, we achieve the sensing user accountability based on digital signature algorithm and credible management. The privacy analysis demonstrates that our scheme satisfies the privacy‐preserving and security requirements. The performance evaluation and experimental result show the efficiency of our scheme in practice.

Secure KNN Classification Scheme Based on Homomorphic Encryption for Cyberspace

With the advent of the intelligent era, more and more artificial intelligence algorithms are widely used and a large number of user data are collected in the cloud server for sharing and analysis, but the security risks of private data breaches are also increasing in the meantime. CKKS homomorphic encryption has become a research focal point in the cryptography field because of its ability of homomorphic encryption for floating-point numbers and comparable computational efficiency. Based on the CKKS homomorphic encryption, this paper implements a secure KNN classification scheme in cloud servers for Cyberspace (CKKSKNNC) and supports batch calculation. This paper uses the CKKS homomorphic encryption scheme to encrypt user data samples and then uses Euclidean distance, Pearson similarity, and cosine similarity to compute the similarity between ciphertext data samples. Finally, the security classification of the samples is realized by voting rules. This paper selects IRIS data set for experimental, which is the classification data set commonly used in machine learning. The experimental results show that the accuracy of the other three similarity algorithms of the IRIS data is around 97% except for the Pearson correlation coefficient, which is almost the same as that in plaintext, which proves the effectiveness of this scheme. Through comparative experiments, the efficiency of this scheme is proved.

Secure K-NN Query on Encrypted Cloud Data with Multiple Keys

Cloud computing has become an undeniably famous assistance for its adaptability and versatility, which rouses numerous associations, foundations and organizations to want to re-appropriate data administrations to cloud stage. Simultaneously, much consideration has been paid to adapt to the extraordinary security and protection issues in recloud cloud. The general methodology is to scramble data by the data owner (DO) before re-appropriating; the approved query user play out an unpredictable arrangement of encryption and decoding tasks during question execution. In any case, the above plans have accepted that the question clients are completely trusted and have the entrance to the key for scrambling and unscrambling recloud data. It will achieve a few issues in reality. So we propose a novel plan for secure KNN inquiry on encoded cloud data with various keys, in which the data owner and each question client all hold their own various keys, and don’t impart them to one another; in the interim, the data owner scrambles and decodes re-appropriated data utilizing the key of his own. Our plan is built by set of conventions to jelly the data classification and question over scrambled cloud data without key-sharing.

B+-Tree Based Multi-Keyword Ranked Similarity Search Scheme Over Encrypted Cloud Data

With the sustained evolution and expeditious popularization of cloud computing, an ever-increasing number of individuals and enterprises are encouraged to outsource data to cloud servers for reducing management overhead and ease of access. Privacy requirements demand encryption of sensitive information before outsourcing, which, on the other hand, diminishes the usability of data and makes considerable efficient keyword search techniques used on plaintext inapplicable. In this paper, we propose a secure multi-keyword ranked search scheme based on document similarity to work out the problem. In order to achieve the goals of multi-keyword search and ranking search results, we adopt the vector space model and TF-IDF model to generate index and query vectors. By introducing the secure kNN computation, index and query vectors can be encrypted to prevent cloud servers from obtaining sensitive frequency information. For the need of efficiency advancement, we adopt the $B^{+}$ -tree as the basic structure to build the index and construct a similar document collection for each document. Due to the use of our unique index structure, compared to linear search, the search efficiency is more exceptional. Extensive experiments on the real-world document collection are conducted to demonstrate the feasibility and efficiency of the proposed solution.

Secure K-Nearest neighbor queries in two-tiered mobile wireless sensor networks

Tiered Mobile Wireless Sensor Network(TMWSN) is a new paradigm introduced by mobile edge computing. Now it has received wide attention because of its high scalability, robustness, deployment flexibility, and it has a wide range of application scenarios. In TMWSNs, the storage nodes are the key nodes of the network and are more easily captured and utilized by attackers. Once the storage nodes are captured by the attackers, the data stored on them will be exposed. Moreover, the query process and results will not be trusted any more. This paper mainly studies the secure KNN query technology in TMWSNs, and we propose a secure KNN query algorithm named the Basic Algorithm For Secure KNN Query(BAFSKQ) first, which can protect privacy and verify the integrity of query results. However, this algorithm has a large communication overhead in most cases. In order to solve this problem, we propose an improved algorithm named the Secure KNN Query Algorithm Based on MR-Tree(SEKQAM). The MR-Trees are used to find the K-nearest locations and help to generate a verification set to process the verification of query results. It can be proved that our algorithms can effectively guarantee the privacy of the data stored on the storage nodes and the integrity of the query results. Our experimental results also show that after introducing the MR-Trees in KNN queries on TMWSNs, the communication overhead has an effective reduction compared to BAFSKQ.

Secure k-ish Nearest Neighbors Classifier

Abstract The k-nearest neighbors (kNN) classifier predicts a class of a query, q, by taking the majority class of its k neighbors in an existing (already classified) database, S. In secure kNN, q and S are owned by two different parties and q is classified without sharing data. In this work we present a classifier based on kNN, that is more efficient to implement with homomorphic encryption (HE). The efficiency of our classifier comes from a relaxation we make to consider κ nearest neighbors for κ ≈k with probability that increases as the statistical distance between Gaussian and the distribution of the distances from q to S decreases. We call our classifier k-ish Nearest Neighbors (k-ish NN). For the implementation we introduce double-blinded coin-toss where the bias and output of the toss are encrypted. We use it to approximate the average and variance of the distances from q to S in a scalable circuit whose depth is independent of |S|. We believe these to be of independent interest. We implemented our classifier in an open source library based on HElib and tested it on a breast tumor database. Our classifier has accuracy and running time comparable to current state of the art (non-HE) MPC solution that have better running time but worse communication complexity. It also has communication complexity similar to naive HE implementation that have worse running time.

A dynamic and verifiable multi-keyword ranked search scheme in the P2P networking environment

With the rapid development of the Internet, Peer to Peer(P2P) network has been applied in various fields. Users in P2P network also have a large amount of data, but users cannot provide enough storage space locally. More and more users choose to upload their own data to cloud server in order to save overhead and facilitate sharing their own data with other users. In order to ensure data security, researchers have proposed searchable encryption(SE) technology, and searchable encryption has been widely used. In this paper, a dynamic verifiable multi-keyword ranked search scheme is proposed under the background of P2P network and cloud storage service(CSS). On the basis of using secure kNN algorithm to encrypt index and traditional inner product algorithm to obtain ranked results, the scheme in this paper realizes forward and backward security by changing the structure of file vector and using modular residual computation. Meanwhile, the integrity and freshness of search results are verified by combining timestamp chain and Merkle tree. Finally, the security of this scheme under two threat models is analyzed, and the performance evaluation experiment is carried out on the document set.

A Privacy-Protected Image Retrieval Scheme for Fast and Secure Image Search

With the development of multimedia technology, the secure image retrieval scheme has become a hot research topic. However, how to further improve algorithm performance in the ciphertext needs to be further explored. In this paper, we propose a secure image retrieval scheme based on a deep hash algorithm for index encryption and an improved 4-Dimensional(4-D)hyperchaotic system. The main contributions of this paper are as follows: (1) A novel secure retrieval scheme is proposed to control data transmission. (2) An improved 4-D hyperchaotic system is proposed to preserve privacy. (3) We propose an improved deep pairwise-supervised hashing (DPSH) algorithm and secure kNN to perform index encryption and propose an improved loss function to train the network model. (4) A secure access control scheme is shown, which aims to achieve secure access for users. The experimental results show that the proposed scheme has better retrieval efficiency and better security.

Security analysis of secure kNN and ranked keyword search over encrypted data

Wong et al. proposed a novel symmetric encryption scheme in which we can find the k-nearest neighbors from encrypted data and an encrypted query. Their scheme uses a pair of encryption functions that has an inner-product preserving property. Because of this property, the pair of encryption functions has been used in several encryption schemes involving ranked multi-keyword search as applications. On the other hand, Yao et al. pointed out that the pair of encryption functions is insecure when the attacker can get plaintext–ciphertext pairs. To prevent this attack, some countermeasures are given in the applications, e.g., randomizing plaintexts before encrypted. In this paper, we reanalyze the security of the inner-product preserving encryption functions. We first discuss the countermeasures against Yao et al.’s attack used in the applications. In particular, we show that one of them is ineffective. Next, we show that the first encryption function is breakable by the known plaintext attack by showing a concrete key-recovery procedure. Unlike Yao et al.’s attack, our attack does not use the second encryption function.

A privacy-preserving image retrieval scheme based secure kNN, DNA coding and deep hashing

In recent years, information leakage incidents occur frequently. Ciphertext domain search has become a hot spot in multimedia technology and software engineering. Therefore, how to mine valuable information safely and effectively is very important. In this paper, we present the CBIR solution and complete the relevant software development. The proposed method not only outsources CBIR services but also avoids privacy leaks. Our main contributions are reflected in two aspects: improving search efficiency and protecting user privacy. For one thing, we propose a novel privacy protection algorithm to encrypt images. For the other thing, we propose an integrated deep hash algorithm to extract the high-level features of images. In the index encryption process, we use secure kNN to encrypt the index. Through experimental analysis and argumentation, the proposed privacy protection algorithm has a lower correlation coefficient and a better information entropy. It is secure enough and can resist common types of attacks encountered during data transmission. The proposed secure image retrieval algorithm is tested on two datasets. Compared with the same type of algorithm, the proposed algorithm has better retrieval performance. In short, the privacy reservation scheme guarantees the security and effectiveness of the system.

Enabling Efficient and Geometric Range Query With Access Control Over Encrypted Spatial Data

As a basic query function, range query has been exploited in many scenarios such as SQL retrieves, location-based services, and computational geometry. Meanwhile, with explosive growth of data volume, users are increasingly inclining to store data on the cloud for saving local storage and computational cost. However, a long-standing problem is that the user’s data may be completely revealed to the cloud server because it has full data access right. To cope with this problem, a frequently-used method is to encrypt raw data before outsourcing them, but the availability and operability of data will be reduced significantly. In this paper, we propose an efficient and geometric range query scheme (EGRQ) supporting searching and data access control over encrypted spatial data. We employ secure KNN computation, polynomial fitting technique, and order-preserving encryption to achieve secure, efficient, and accurate geometric range query over cloud data. Then, we propose a novel spatial data access control strategy to refine user’s rights in our EGRQ. To improve the efficiency, R-tree is adopted to reduce the searching space and matching times in whole search process. Finally, we theoretically prove the security of our proposed scheme in terms of confidentiality of spatial data, privacy protection of index and trapdoor, and the unlinkability of trapdoors. In addition, extensive experiments demonstrate the high efficiency of our proposed model compared with existing schemes.

Secure and Dynamic Multi-Keyword Ranked Search Scheme Over Encrypted Cloud Data Using K-Means Clustering

A Secure and Dynamic Multi-keyword graded Search theme over Encrypted Cloud information attributable to the increasing fame of cloud computing, a lot of information homeowners are spurred to source their information to cloud servers for unimaginable accommodation and diminished expense in information management can also perform information dynamic operations on files. On the opposite hand, sensitive information needs to be encrypted before outsourcing for security conditions, that obsoletes information use like keyword-based document retrieval. A protected multi-keyword graded search theme over encrypted cloud information, that all the whereas underpins part update operations like deletion and insertion of documents. Especially, the vector area model and therefore the usually utilised TF_IDF model are consolidated as a neighbourhood of the index development and question generation. A unique tree-based index structure employing a "K-means Clustering" formula to provide practiced multi-keyword graded search. The secure KNN formula is employed to cipher the index and question vectors, so guarantee precise importance score calculation between encrypted index and question vectors. With a selected finish goal to oppose measurable attacks, phantom terms are accessorial to the index vector for glaring search results. Due to the employment of our exceptional tree-based index structure. Keyword: Reduplication, Authorized duplicate check, public auditing, shared data, Cloud computing.

A Privacy-Preserving Handwritten Signature Verification Method Using Combinational Features and Secure KNN

The handwritten signature is a widely accepted biometric trait for the individual authentication. This paper proposes a secure and dynamic signature verification method which applies to the mobile phone. A key point for signature verification is to extract features of good distinguishability. There are four main steps in our feature extraction process, including preprocessing, attribute generation, attribute truncation and quantization, and feature generation. In addition to the global features that are extracted from the whole signature, we divide a signature into several segments and extract features from these separate regions. The final feature vector is the combination of global and regional features. A user template is constructed by averaging the feature vectors whose elements are scaled by the feature-specific factors. Then, the similarity score of the test signature to the user template can be measured by Euclidean distance. In order to protect the user privacy in a cloud computing scenario, the template and features are further protected by secure kNN which has no influence on signature matching. The performance of the proposed method is demonstrated on the SG-NOTE database acquired by Samsung Galaxy Note and the MCYT-100 database captured by a WACOM pen tablet.

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.