Searchable Encryption Technique Research Articles

Encrypted Video Search with Single/Multiple Writers

Video-based services have become popular. Clients often outsource their videos to the cloud to relieve local maintenance. However, privacy has become a major concern since many videos contain sensitive information. Although retrieving (unencrypted) videos has been extensively investigated, retrieving encrypted multimedia has received relatively rare attention, at best in a limitation of image-based similarity searches. We initiate the study of scalable encrypted video search, enabling clients to query videos similar to an image search. Our modular framework leverages intrinsic attributes of videos, such as semantics and visuals, to effectively capture their contents. We propose a two-step approach whereby lightweight searchable encryption techniques are used for pre-screening, followed by an interactive approach for fine-grained search. Furthermore, we present three instantiations, including one centralized-writer instantiation and two distributed-writer instantiations, to effectively cater to varying needs and scenarios– 1) The centralized one employs forward and backward private searchable encryption [CCS 2017] over deep hashing [CVPR 2020]. 2) Motivated by distributed computing, the multi-writer instantiations building atop HSE [Usenix Security 2022] allows searching the relevant videos contributed by multiple intuitions collaboratively. Our experimental results illustrate their practical performance over multiple real-world datasets, whether in a centralized setting or distributed setting.

Aiding secure data retrieval incorporated with parallelization technique in cloud

<p>Cloud data owners prefer to outsource data because of ease in maintenance. Data confidentiality of this outsourced sensitive data is a major task. The searchable encryption technique helps to carry out searches on encrypted data without decrypting it. In the data outsourcing environment, volume of data is increasing rapidly. Hence the time required to build the index and to carry out searches is also increasing exponentially. This makes it more difficult to build a system which is efficient, reliable and can cope up with growing data. In this paper, a parallelization technique to build the index on outsourced data is proposed. This technique minimizes the time required to construct the index. It also supports secure ranked retrieval using bucketization technique. The buckets are formed using Hadoop map reduce framework which achieves significant efficiency. The proposed method prune the keyword dataset, which helps in significant reduction in the size of index. Through extensive experiments using standard dataset, the performance of the system is validated. The experimental results show that the proposed system requires less time for index construction and hence improves retrieval efficiency.</p>

Fast and private multi-dimensional range search over encrypted data

For businesses looking to outsource their data to remote servers, cloud-based data storage is a popular choice. It is popular due to its flexibility, cost-effectiveness, and widespread availability. However, ensuring the confidentiality of data is a critical challenge that must be addressed. As a response to this issue, searchable encryption techniques have been developed. These techniques enable search queries to be performed on encrypted data while still keeping the plaintext confidential. While most existing symmetric searchable encryption schemes are designed for one-dimensional data records or document-keyword inverted indices, this paper introduces MDRSSE, a novel symmetric searchable encryption scheme specifically tailored for multi-dimensional range search. MDRSSE stands out as one of the pioneering SSE schemes to support multi-dimensional range search efficiently, without incurring undetermined additional communication or computation costs. By employing a single round of communication between the client and server, MDRSSE enables an honest-but-curious server to respond to multi-dimensional range queries without gaining knowledge of the data records or revealing the search query. Notably, MDRSSE boasts the lowest overall search complexity compared to existing state-of-the-art symmetric searchable encryption schemes designed for multi-dimensional range search. Extensive experimental tests were conducted to validate the robustness and practicality of our proposed scheme. The results demonstrate that, for a dataset consisting of 100K records with 12 dimensions (with each leaf node holding 500 records), it takes only 2.2 seconds to generate the encrypted dataset, and the overall setup phase completes within 2.5 seconds. Furthermore, for a range query encompassing 50 nodes, the search time is less than 2 ms and 3 ms for the client and server, respectively. MDRSSE achieves semantic security under the IND-CPA assumption, all without requiring additional storage size at the server.

Survey on Secure Keyword Search over Outsourced Data: From Cloud to Blockchain-assisted Architecture

Secure keyword search is a prevailing search service offered in outsourced environments. However, with the increasingly severe security vulnerabilities of conventional centralized outsourcing, the architecture of secure keyword search, with searchable encryption (SE) as the underlying technique, has recently shifted from cloud-centered models to blockchain-assisted models. Existing surveys commonly fail to capture such an evolution and the corresponding benefits. What on earth does blockchain bring about and what are the unexplored challenges? This survey provides a systematic review of secure keyword search over outsourced data from cloud to blockchain-assisted architectures. We propose a taxonomy assorting present studies, depending on whether cloud/blockchain and data sharing are included, in which blockchain-assisted architecture is further divided into blockchain-side and cloud-side keyword search, respectively. Technically, we conclude five types of representative SE techniques with fitting architectures, either cryptographic-based or hardware-dependent. Notably, we propose comprehensive methodologies to select relevant papers, discuss, and compare existing schemes regarding functionalities, security, efficiency, and fairness (up to 21 compared items). Finally, open issues and potential research directions are identified for future work. We aspire to help pave the way for addressing the theoretical and empirical aspects of secure keyword search and full-fledged real-world implementation of blockchain-based keyword search applications.

EFFICIENT AND EXPRESSIVE SEARCH OVER ENCRYPTED DATA WITH MULTI FACTOR AUTHENTICATION SCHEME

More data owners are tempted to outsource their data to cloud services as a result of the development of cloud storage. Sensitive data should be encrypted before being outsourced due to privacy concerns. To assure data availability, a variety of searchable encryption techniques are used. However, the effectiveness of data consumers' queries is not given much consideration by the current search algorithms, particularly in the case of several owners. In this study, linguistic coefficients are used to create an encrypted search index. In particular, the encrypted search index utilizing Lagrange co-efficient makes use of secure inner-product calculation for both search and relevance assessment by taking into account a sizable amount of data in the cloud. A system architecture of preferred search over encrypted data in the cloud scene is built, and the requirements in terms of efficiency and privacy are specified. This enables the clo ud servers to do a secure search without knowing any sensitive data (e.g., keywords and trapdoors).For each data owner, the Lagrange coefficient is used to build indices that can support searches over numerous keyword fields and enable accu rate relevance computation in order to produce an efficient search.To protect user data privacy, a multifactor authenticati on technique might be utilised.After logging in to the system, the user will receive three different sorts of authentications, including an OTP utilizing a number, a captcha, and an OTP image. This picture will serve as a key. In order to accomplish secure search and relevance score calculation, the user's preference and the search query are expressed in vector form. Additionally, Preferred Search over Encrypted Data (PSED) is consistent with cloud computing's scalability and adaptability. Finally, a thorough security study verifies the security of our scheme, and a performance analysis highlights its effectiveness and efficiency. Keywords: Encrypted Data, Inner product calculation, Multifactor authentication, Data privacy

Security-enhanced public-key authenticated searchable encryption

Public key authenticated encryption with keyword search (PAEKS) is an asymmetric searchable encryption technique secure against inside keyword guessing attacks. The security properties of PAEKS are modeled via ciphertext indistinguishability (CI) and trapdoor indistinguishability (TI). Nowadays, CI security has been extended to enable the attacker to challenge two keyword tuples (MCI security), and even obtain ciphertext embedded with any keyword (fully MCI security). However, similar extensions to TI security have not yet been achieved from bilinear pairings.In this work, we address this problem by focusing not only on ciphertext privacy, but also on trapdoor privacy. In particular, we adopt the enhanced TI-security, namely fully MTI security. We begin by conducting a detailed comparison of the fully MTI security with current TI models. Subsequently, we design a PAEKS scheme from bilinear pairings that initially achieves both fully MTI and fully MCI security, thereby offering a stronger security guarantee than other existing PAEKS schemes. To optimize the practical efficiency, we adopt various techniques: accelerating the online efficiency by shifting most computational cost offline; leveraging parallel computing to speed up the test algorithm. Consequently, our optimized scheme is competitive with other PAEKS schemes optimized using similar techniques.

Supporting secure dynamic alert zones using searchable encryption and graph embedding

Location-based alerts have gained increasing popularity in recent years, whether in the context of healthcare (e.g., COVID-19 contact tracing), marketing (e.g., location-based advertising), or public safety. However, serious privacy concerns arise when location data are used in clear in the process. Several solutions employ searchable encryption (SE) to achieve secure alerts directly on encrypted locations. While doing so preserves privacy, the performance overhead incurred is high. We focus on a prominent SE technique in the public-key setting–hidden vector encryption, and propose a graph embedding technique to encode location data in a way that significantly boosts the performance of processing on ciphertexts. We show that the optimal encoding is NP-hard, and we provide three heuristics that obtain significant performance gains: gray optimizer, multi-seed gray optimizer and scaled gray optimizer. Furthermore, we investigate the more challenging case of dynamic alert zones, where the area of interest changes over time. Our extensive experimental evaluation shows that our solutions can significantly improve computational overhead compared to existing baselines.

ShieldDB: An Encrypted Document Database With Padding Countermeasures

Cloud storage systems have seen a growing number of clients due to the fact that more and more businesses and governments are shifting away from in-house data servers and seeking cost-effective and ease-of-access solutions. However, the security of cloud storage is underestimated in current practice, which resulted in many large-scale data breaches. To change the status quo, this paper presents the design of ShieldDB, an encrypted document database. ShieldDB adapts the searchable encryption technique to preserve the search functionality over encrypted documents without having much impact on its scalability. However, merely realising such a theoretical primitive suffers from real-world threats, where a knowledgeable adversary can exploit the leakage (aka access pattern to the database) to break the claimed protection on data confidentiality. To address this challenge in practical deployment, ShieldDB is designed with tailored padding countermeasures. Unlike prior works, we target a more realistic adversarial model, where the database gets updated continuously, and the adversary can monitor it at an (or multiple) arbitrary time interval(s). ShieldDB’s padding strategies ensure that the access pattern to the database is obfuscated all the time. We present a full-fledged implementation of ShieldDB and conduct intensive evaluations on Azure Cloud.

Encryption Scheme of Verifiable Search Based on Blockchain in Cloud Environment

While transferring data to cloud servers frees users from having to manage it, it eventually raises new problems, such as data privacy. The concept of searchable encryption has drawn more and more focus in research as a means of resolving the tension between data accessibility and data privacy. Due to the lack of integrity and correctness authentication in most searchable encryption techniques, malicious cloud servers may deliver false search results to users. Based on public key encryption with searching (PEKS), the study suggests a privacy-preserving method for verifiable fuzzy keyword searches based on the Ethernet blockchain in a cloud context to overcome the aforementioned security concerns. The search user can check the accuracy and integrity of the query document using the unalterability characteristics of the Ethernet blockchain system in this scheme to prevent the cloud server from giving incorrect query results. Furthermore, a fair transaction between the cloud server and the data user is achieved and can be tracked back to the malicious user using hash functions and Ethereum smart contracts, even if the user or the cloud is malicious. Finally, the security analysis shows that, under the random oracle model, our technique fulfils the adaptive selection keyword’s semantic security. The performance assessment demonstrates that the proposed scheme outperforms other related schemes in terms of computational efficiency.

Privacy-preserving data dissemination scheme based on Searchable Encryption, publish–subscribe model, and edge computing

Attribute-based Searchable Encryption is emerging as a promising cryptographic technique supporting data protection, flexible access control, and keyword search over encrypted data. The current scientific literature already investigated its adoption in cloud-based services and additionally explored the usage of edge computing to implement some of the cryptographic tasks in scenarios with limited computational capabilities (such as Internet of Things). In the majority of the available solutions, however, the remote cloud is still responsible for data storage, keyword search over encrypted data, and delivery tasks. Indeed, the heavy computational load generated in scenarios with multiple data producers and data consumers (never studied yet) and large communication latencies can inevitably compromise the overall system performance. To bridge this gap, this work proposes a decentralized service architecture offering privacy-preserving data dissemination, by jointly leveraging attribute-based Searchable Encryption techniques, publish–subscribe communication model, and edge computing capabilities. Here, customized Edge Servers are deployed at the network edge to (i) collect subscription requests encoded via Searchable Encryption Trapdoors, (ii) receive data publications, encrypted via Attribute-based Searchable Encryption scheme, (iii) implement keyword search over encrypted data, and (iv) deliver encrypted data only to authorized requesters. Experimental tests explored the impact of network configuration and traffic load on both communication latency and energy consumption. Obtained results demonstrated the unique ability of the proposed solution to achieve shorter data delivery delays as well as less energy consumption with respect to cloud-based alternatives.

On the Performance of Secure Sharing of Classified Threat Intelligence between Multiple Entities

The sharing of cyberthreat information within a community or group of entities is possible due to solutions such as the Malware Information Sharing Platform (MISP). However, the MISP was considered limited if its information was deemed as classified or shared only for a given period of time. A solution using searchable encryption techniques that better control the sharing of information was previously proposed by the same authors. This paper describes a prototype implementation for two key functionalities of the previous solution, considering multiple entities sharing information with each other: the symmetric key generation of a sharing group and the functionality to update a shared index. Moreover, these functionalities are evaluated regarding their performance, and enhancements are proposed to improve the performance of the implementation regarding its execution time. As the main result, the duration of the update process was shortened from around 2922 s to around 302 s, when considering a shared index with 100,000 elements. From the security analysis performed, the implementation can be considered secure, thus confirming the secrecy of the exchanged nonces. The limitations of the current implementation are depicted, and future work is pointed out.

Secure Content Based Image Retrieval Scheme Based on Deep Hashing and Searchable Encryption

To solve the problem that the existing ciphertext domain image retrieval system is challenging to balance security, retrieval efficiency, and retrieval accuracy. This research suggests a searchable encryption and deep hashing-based secure image retrieval technique that extracts more expressive image features and constructs a secure, searchable encryption scheme. First, a deep learning framework based on residual network and transfer learning model is designed to extract more representative image deep features. Secondly, the central similarity is used to quantify and construct the deep hash sequence of features. The Paillier homomorphic encryption encrypts the deep hash sequence to build a high-security and low-complexity searchable index. Finally, according to the additive homomorphic property of Paillier homomorphic encryption, a similarity measurement method suitable for computing in the retrieval system’s security is ensured by the encrypted domain. The experimental results, which were obtained on Web Image Database from the National University of Singapore (NUS-WIDE), Microsoft Common Objects in Context (MS COCO), and ImageNet data sets, demonstrate the system’s robust security and precise retrieval, the proposed scheme can achieve efficient image retrieval without revealing user privacy. The retrieval accuracy is improved by at least 37% compared to traditional hashing schemes. At the same time, the retrieval time is saved by at least 9.7% compared to the latest deep hashing schemes.

Modern symmetric searchable encryption technique for string classification in cloud technology

Modern symmetric searchable encryption technique for string classification in cloud technology

Provably throttling SQLI using an enciphering query and secure matching

Web applications, which dominate the internet, act as communication media between customers and service providers. Web applications are an internet innovation that provide customer services such as e-banking, e-commerce and e-booking. Developing web applications has become increasingly complicated because of security threats and service issues that involve valuable information. Attack methods such as structured query language (SQL) injection insert malicious code within user input data requests to gain unauthorised access, and then the attacker targets a database to manipulate information. In this paper, we propose a prevention method against SQL injection attacks through cryptography and searchable encryption. The proposed method uses a cryptography technique to encrypt all database information, where each piece of user information is encrypted with a separate key. The rest of the database information is ciphered with secret keys, and a searchable encryption technique is used for other database operations to preserve privacy. The login process compares the ciphered username from the database and user entry to authenticate the user. The proposed method is implemented on the PHP and MySQL databases, which are open-source applications. The results show efficient prevention of SQL injection, and the database remains protected against SQL injection attacks

Research on the main methods and schemes of encryption with search capability

The growing popularity of data outsourcing to third-party cloud servers causes their owners to have serious concerns about their security due to possible data leakage. A well-known measure to solve this problem and ensure the confidentiality of data is to encrypt it. However, the use of traditional encryption techniques is faced with the problem of how to allow untrusted cloud servers to perform search operations, while the actual data transmitted must remain confidential. Searchable encryption is a powerful tool, a class of cryptographic techniques that attempts to solve this problem. Searchable encryption acts as a data management technique that allows data owners to store and manage their data on a third-party, untrusted cloud server, and allows the data user to delegate search functions to the cloud server to retrieve that data. Currently, there are a number of approaches to solving this problem, although there is still no dominant solution. Therefore, the paper presents an overview of current secure search solutions. The main searchable encryption techniques are considered, which allow you to perform search operations on encrypted data without disclosing any information about what is being searched. The strengths and weaknesses of the analyzed methods are highlighted. Models and architectures of existing secure search engines are analyzed, taking into account the peculiarities of their operation scenarios. The problem of confidentiality in searchable encryption schemes is discussed. A comparative analysis of the performance of several searchable symmetric encryption schemes is given. Various gaps in the area under consideration are identified, with indication of open research problems.

Dual-Server Public-Key Authenticated Encryption with Keyword Search

In cloud storage, how to search sensitive data efficiently and securely is a challenging problem. The searchable encryption technique provides a secure storage method without loss of data confidentiality and usability. As an important branch of searchable encryption, public-key encryption with keyword search (PEKS) is widely studied by scholars. However, most of the traditional PEKS schemes are vulnerable to the inside keyword guessing attack (IKGA). Resisting the inside keyword guessing attack is likely to become an essential property of all new PEKS schemes. For a long time, mitigating IKGA has been inefficient and difficult, and most existing PEKS schemes fail in achieving their security goals. To address the above problems, we define the notion of <b>D</b>ual-server <b>P</b>ublic-key <b>A</b>uthenticated <b>E</b>ncryption with <b>K</b>eyword <b>S</b>earch (DPAEKS), which protects against IKGA by leveraging two servers that do not cooperate, and supports the authentication property. Then, we provide a construction of DPAEKS without bilinear pairings. Experimental results obtained using a real-world dataset show that our scheme is highly efficient and provides strong security, making it suitable for deployment in practical applications.

Multi-key searchable encryption technique for index-based searching

Multi-key searchable encryption technique for index-based searching

BFR-SE: A Blockchain-Based Fair and Reliable Searchable Encryption Scheme for IoT with Fine-Grained Access Control in Cloud Environment

Due to capacity limitations, large amounts of data generated by IoT devices are often stored on cloud servers. These data are usually encrypted to prevent the disclosure, which significantly affects the availability of this data. Searchable encryption (SE) allows a party to store his data created by his IoT devices or mobile in encryption on the cloud server to protect his privacy while retaining his ability to search for data. However, the general SE techniques are all pay-then-use. The searchable encryption service providers (SESP) are considered curious but honest, making it unfair and unreliable. To address these problems, we combined ciphertext-policy attribute-based encryption, Bloom filter, and blockchain to propose a blockchain-based fair and reliable searchable encryption scheme (BFR-SE) in this paper. In BFR-SE, we constructed an attribute-based searchable encryption model that can provide fine-grained access control. The data owner stores the indices on SESP and stores some additional auxiliary information on the blockchain. After a data user initiates a request, SESP must return the correct and integral search results before the deadline. Otherwise, the data user can send an arbitration request, and the blockchain will make a ruling. The blockchain will only perform arbitrations based on auxiliary information when disputes arise, saving the computing resources on-chain. We analyzed the security and privacy of BFR-SE and simulated our scheme on the EOS blockchain, which proves that BFR-SE is feasible. Meanwhile, we provided a thorough analysis of storage and computing overhead, proving that BFR-SE is practical and has good performance.

A Blockchain-Based Medical Data Sharing Mechanism with Attribute-Based Access Control and Privacy Protection

The rapid development of wearable sensors and the 5G network empowers traditional medical treatment with the ability to collect patients’ information remotely for monitoring and diagnosing purposes. Meanwhile, the health-related mobile apps and devices also generate a large amount of medical data, which is critical for promoting disease research and diagnosis. However, medical data is too sensitive to share, which is also a common issue for IoT (Internet of Things) data. The traditional centralized cloud-based medical data sharing schemes have to rely on a single trusted third party. Therefore, the schemes suffer from single-point failure and lack of privacy protection and access control for the data. Blockchain is an emerging technique to provide an approach for managing data in a decentralized manner. Especially, the blockchain-based smart contract technique enables the programmability for participants to access the data. All the interactions are authenticated and recorded by the other participants of the blockchain network, which is tamper resistant. In this paper, we leverage the K-anonymity and searchable encryption techniques and propose a blockchain-based privacy-preserving scheme for medical data sharing among medical institutions and data users. To be specific, the consortium blockchain, Hyperledger Fabric, is adopted to allow data users to search for encrypted medical data records. The smart contract, i.e., the chaincode, implements the attribute-based access control mechanisms to guarantee that the data can only be accessed by the user with proper attributes. The K-anonymity and searchable encryption ensure that the medical data is shared without privacy leaking, i.e., figuring out an individual patient from queries. We implement a prototype system using the chaincode of Hyperledger Fabric. From the functional perspective, security analysis shows that the proposed scheme satisfies security goals and precedes others. From the performance perspective, we conduct experiments by simulating different numbers of medical institutions. The experimental results demonstrate that the scalability and performance of our scheme are practical.

Securing smart healthcare system with edge computing

Nowadays, the healthcare system faces new challenges due to the increasing growth of sensitive patient data. There is a need for a Smart Healthcare System (SHS) that makes the healthcare system smarter to protect the data and maintain privacy. An SHS contains several IoT devices, sensors, and mobile nodes that collect the data from the patient’s body and store it remotely. The most commonly used approach for storing data in the SHS is cloud computing-based storage. This type of system is very popular and provides common solutions to all users because it allows accessing the data remotely. The security and privacy of remotely located data is another important concern because the solution is available to all types of users. Remotely located data enables a lack of control over the data. The real-time health monitoring system based on cloud computing is not feasible because the response time is comparatively high. It is essential to provide some solution that reduces this response time. The edge computing approach reduces the response time in real-time cases as well as needs low bandwidth.This paper proposed a smart healthcare system based on edge computing architecture. This architecture consists of an intermediary layer called an edge computing layer responsible for maintaining the network latency and preserving the privacy of the patient data. This edge computing layer has handled the encryption and privacy of the patient data with the help of the Privacy-Preserving Searchable Encryption (PPSE) technique. The access control mechanism is also implemented to restrict unauthorized access to the remote stored patient data. The proposed model’s implementation, performance, and security analysis show high security, low latency, low transfer time, low power, and low energy compared to a similar approach. The computed results demonstrated transfer time (64.24%), power consumption (69.03%), and energy consumption (69.56%) is reduced using the edge computing approach.