Privacy-preserving Keyword Search Research Articles

Securely sharing outsourced IoT data: A secure access and privacy preserving keyword search scheme

The rapid progress in the field of IoT and its wide-ranging applications emphasize the criticality of robust security measures for effectively sharing, storing, and managing sensitive data generated by IoT devices. Regulations such as the Consumer Data Rights (CDR) highlight the need for the seamless sharing of sensitive data with authorized third parties while ensuring confidentiality and privacy. To enable such secure sharing, a data storage and sharing scheme should fulfill the following core requirements: (a) support multi-client data sharing settings, allowing IoT data owners to authorize multiple clients; (b) a dynamic storage environment permitting IoT owners to add or remove files with minimal privacy leak; (c) decentralized storage for distributing data across servers or Cloud Service Providers (CSPs) for greater security; and (d) efficient privilege revocation mechanism which incurs less computation and communication overhead. To address these requirements, we have proposed a novel keyword search scheme using computationally lightweight cryptographic primitives. Our scheme empowers IoT data owners to securely share, store and manage encrypted data in the CSPs, providing better security and privacy. We have provided formal security proof for our scheme as well as validated its efficiency via extensive experiments on the Docker platform. On a database of 12 million keyword/document pairs (with 105 documents and 103 keywords), our scheme took about 18 ms to return all matched documents.

PUBLIC KEY ENCRYPTION SCHEME BASED ON KEYWORD SEARCH

This study explores the concept of a Public Key Encryption Scheme (PKES) with an emphasis on enabling keyword search functionality. The aim of this research is to develop a cryptographic framework that allows users to securely search over encrypted data without revealing sensitive information. The study investigates various methods and techniques employed in PKES to achieve efficient and privacy-preserving keyword search capabilities. The research highlights the increasing importance of data privacy and the need for secure information retrieval over encrypted data. Traditional encryption methods hinder search functionality, making it challenging to perform keyword searches on encrypted data. To address this limitation, the study focuses on PKES, which facilitates search operations while preserving the confidentiality of the underlying data. Experimental research design was used for this research. To accomplish the objectives, the study employs a combination of cryptographic algorithms and data transformation techniques. Public key encryption algorithms are utilized to secure the data, ensuring that only authorized users can access the information. Additionally, searchable encryption methods, such as Searchable Symmetric Encryption (SSE) and Index-Based Encryption (IBE), are explored to enable efficient and secure keyword searches on encrypted data. The results of the research demonstrate the feasibility and effectiveness of the proposed PKES with keyword search. The developed framework allows users to encrypt their data and store it securely, while still being able to search for specific keywords without decrypting the data. The experiments conducted on different datasets showcase the efficiency of the implemented techniques, providing a practical solution for secure data retrieval. In conclusion, the study presents a robust and privacy-preserving Public Key Encryption Scheme that incorporates keyword search capabilities. The research contributes to the field of cryptography by addressing the limitations of traditional encryption methods and offering an effective solution for secure information retrieval. The findings highlight the significance of PKES in safeguarding sensitive data while allowing users to perform keyword searches conveniently

Lightweight and Privacy-Preserving Multi-Keyword Search over Outsourced Data

In cloud computing, documents can be outsourced to the cloud server to achieve flexible access control and efficient sharing among multiple users. The outsourced documents can be intelligently searched according to some keywords with the help of cloud server. During the search process, some private information of outsourced documents may be leaked since the keywords may contain sensitive information of users. However, existing privacy-preserving keyword search schemes have high computation complexity, which are not suitable for resource-constrained end devices—that is, the data processing and search trapdoor generation procedures require users to take resource-intensive computations, e.g., high-dimensional matrix operations, exponentiations and bilinear pairings, which are unaffordable by resource-constrained devices. To address the issues of efficiency and privacy for realizing sorted multi-keyword search over outsourced data in clouds, this paper proposes a lightweight privacy-preserving ranked multi-keyword search (PRMS) scheme, which is further extended to allow each outsourced document to be associated with multiple types of keywords. The searched documents can be sorted according to their similarity scores between the search query and the keyword index of documents, so that only when the similarity score exceeds a given threshold, the corresponding searched document will be returned. The security analysis demonstrates that the proposed PRMS schemes can guarantee the privacy of outsourced documents and keywords, and provides unlinkability for search trapdoors. Performance analysis and comparison show the practicality of the proposed PRMS schemes.

Security and Privacy Preserving Keyword Search for Cipher Text Retrieval in Cloud Computing based on Oppositional Grasshopper optimization

Security and Privacy Preserving Keyword Search for Cipher Text Retrieval in Cloud Computing based on Oppositional Grasshopper optimization

An Effective and Efficient Framework for Fast Privacy-Preserving Keyword Search on Encrypted Outsourced Cloud Data

Cloud providers offer storage as a service to the data owners to store emails and files on the cloud server. However, sensitive data should be encrypted before storing on the cloud server to avoid privacy concerns. With the encryption of documents, it is not feasible for data owners to retrieve documents based on keyword search as they can do with plain text documents. Hence, it is desirable to perform a multi-keyword search on encrypted data. To achieve this goal, we present a fast privacy-preserving model for keyword search on encrypted outsourced data in this paper. Specifically, the model first performs a keyword search on encrypted data and checks its support for dynamic operations. Based on keyword search results, it then sorts all the relevant data documents using the number of keywords matched for a given query. To evaluate its performance of our model, we applied the standard metrics like precision and recall. The results show the effectiveness of our privacy-preserving keyword search on encrypted outsourced data.

Towards fully verifiable forward secure privacy preserving keyword search for IoT outsourced data

As cloud computing and Internet of Things (IoT) are becoming more and more deeply integrated, the new secure challenge for IoT data has attracted great attention. Privacy preserving keyword search, as one important secure technique, achieves search over encrypted cloud-based IoT data. Forward security and verifiability are two important security properties for privacy preserving keyword search for cloud-based IoT data. Forward security makes privacy preserving keyword search schemes for cloud-based IoT data able to resist file injection attack, which is one new attack on privacy preserving keyword search when IoT data is updated. Verifiability makes privacy preserving keyword search schemes for cloud-based IoT data able to ensure the validity of the search results returned by cloud. However, to the best of our knowledge, all of existing privacy preserving keyword search schemes at most achieve forward security and partial verifiability. In order to solve this problem, we explore how to simultaneously achieve the forward security and the full verification for intelligent encrypted data processing in cloud-based IoT and propose the first fully verifiable forward secure privacy preserving keyword search scheme for IoT outsourced data. In order to achieve forward security, we construct the secure index based on the hash chain. In each update, the IoT data owner randomly chooses a string as a new state to generate the new trapdoor. The new trapdoor cannot match previous trapdoors. In order to achieve the full verifiability while still maintaining forward security for intelligent encrypted data processing in cloud-based IoT, we design novel authentication construction called Forward Secure Accumulative Authentication Tag (FSAAT). This kind of tag has the incremental property and supports the verification on the basis of efficient data update. We prove that our scheme is secure and efficient through detailed security analysis and experiments.

A Lightweight Privacy Preserving Keyword Search Over Encrypted Data in Cloud Computing

With the emerging development of cloud computing services, data owners outsource their documents to Cloud Service Providers (CSP) which could lead to threats related to security and privacy. Hence, protecting the privacy of user data and providing queries privacy becomes one of the main concerns of the data owner. One of the solutions for providing privacy and confidentiality of the outsourced data is encrypting it before sending it to the cloud. Although this solution satisfies data confidentiality and prevents the CSP from reading or modifying the data without the data owner's permission, it prevents the data owner to search the outsourced documents directly. Symmetric encryption algorithms e.g. AES have a searching limitation, in which the whole encrypted document needs to be retrieved from the CSP and then decrypt before performing the search procedure. To overcome this limitation, a lot of keyword-based search approaches have been done. These approaches allow users to retrieve just those documents contain special keywords. However, most of these approaches suffer from privacy and security problems and are based on high overhead asymmetric encryption algorithms. This paper proposes a privacy-preserving keyword search scheme for searching over encrypted data. To avoids the high computational cost of asymmetric encryption, the proposed scheme employs symmetric encryption and Bloom filter. Experimental results demonstrate that the proposed searchable encryption algorithm is secure and lightweight, and it has the ability to perform a keyword search over encrypted data without decrypting them.

Privacy Preserving Keyword Search for Multiple Data Users in Cloud

Privacy Preserving Keyword Search for Multiple Data Users in Cloud

PIMRS: achieving privacy and integrity-preserving multi-owner ranked-keyword search over encrypted cloud data

Because of the flexibility and convenience brought by cloud computing, it has been adopted in many applications. To preserve the privacy of cloud data, data owner often encrypts all sensitive data files, which makes the keyword search application based on plaintext a very challenging task. Therefore, several privacy-preserving keyword search algorithms have been developed recently, and most of these works support only single-data-owner settings. However, there are always more than one data owners in real applications, which are much more complex and challenging than single-owner scenario. To support multi-owner keyword search, those prior search algorithms need to be repeated several times, because each data owner intends to encrypt his own files with a unique private-key separately. It is absolutely not an efficient way. In this paper, we propose a privacy and integrity-preserving multi-owner ranked-keyword search scheme termed PIMRS. In the PIMRS, we exploit an asymmetric scalar-product encryption function based on the TF×IDF rule to preserve data privacy and to obtain more precise search results. Furthermore, a circular bi-direction-linked list based scheme is proposed to preserve the integrity of search results, which also enables the misbehaviors of cloud server to be detected. The security analysis of PIMRS shows its privacy and integrity property, and extensive experiments based on real-world data set confirm the high efficiency of proposed schemes. Copyright © 2016 John Wiley & Sons, Ltd.

Implementation of searchable symmetric encryption for privacy-preserving keyword search on cloud storage

AbstractEnsuring the cloud data security is a major concern for corporate cloud subscribers and in some cases for the private cloud users. Confidentiality of the stored data can be managed by encrypting the data at the client side before outsourcing it to the remote cloud storage server. However, once the data is encrypted, it will limit server’s capability for keyword search since the data is encrypted and server simply cannot make a plaintext keyword search on encrypted data. But again we need the keyword search functionality for efficient retrieval of data. To maintain user’s data confidentiality, the keyword search functionality should be able to perform over encrypted cloud data and additionally it should not leak any information about the searched keyword or the retrieved document. This is known as privacy preserving keyword search. This paper aims to study privacy preserving keyword search over encrypted cloud data. Also, we present our implementation of a privacy preserving data storage and retrieval system in cloud computing. For our implementation, we have chosen one of the symmetric key primitives due to its efficiency in mobile environments. The implemented scheme enables a user to store data securely in the cloud by encrypting it before outsourcing and also provides user capability to search over the encrypted data without revealing any information about the data or the query.

Efficient secure-channel free public key encryption with keyword search for EMRs in cloud storage.

Searchable encryption is an important cryptographic primitive that enables privacy-preserving keyword search on encrypted electronic medical records (EMRs) in cloud storage. Efficiency of such searchable encryption in a medical cloud storage system is very crucial as it involves client platforms such as smartphones or tablets that only have constrained computing power and resources. In this paper, we propose an efficient secure-channel free public key encryption with keyword search (SCF-PEKS) scheme that is proven secure in the standard model. We show that our SCF-PEKS scheme is not only secure against chosen keyword and ciphertext attacks (IND-SCF-CKCA), but also secure against keyword guessing attacks (IND-KGA). Furthermore, our proposed scheme is more efficient than other recent SCF-PEKS schemes in the literature.

Cloud-Assisted Mobile-Access of Health Data With Privacy and Auditability

Motivated by the privacy issues, curbing the adoption of electronic healthcare systems and the wild success of cloud service models, we propose to build privacy into mobile healthcare systems with the help of the private cloud. Our system offers salient features including efficient key management, privacy-preserving data storage, and retrieval, especially for retrieval at emergencies, and auditability for misusing health data. Specifically, we propose to integrate key management from pseudorandom number generator for unlinkability, a secure indexing method for privacy-preserving keyword search which hides both search and access patterns based on redundancy, and integrate the concept of attribute-based encryption with threshold signing for providing role-based access control with auditability to prevent potential misbehavior, in both normal and emergency cases.

Privacy-preserving data utilization in hybrid clouds

As cloud computing becomes prevalent, more and more sensitive data is being centralized into the cloud, which raises a new challenge on how to utilize the outsourced data in a privacy-preserving manner. Although searchable encryption allows for privacy-preserving keyword search over encrypted data, it could not work effectively for restricting unauthorized access to the outsourced private data. In this paper, aiming at tackling the challenge of privacy-preserving utilization of data in cloud computing, we propose a practical hybrid architecture in which a private cloud is introduced as an access interface between the data owner/user and the public cloud. Under this architecture, a data utilization system is provided to achieve both exact keyword search and fine-grained access control over encrypted data. Security and efficiency analysis for the proposed system are presented in detail. Then, further enhancements for this system are considered in two steps. (1) We show how to extend our system to support efficient fuzzy keyword search while overcoming the disadvantage of insignificant decryption in the existing privacy-preserving fuzzy keyword search scheme. (2) We demonstrate approaches to realize an outsourcing cryptographic access control mechanism and further reduce the computational cost at the data user side.