Multi-owner Secure Encrypted Search Using Searching Adversarial Networks
Nanjing University of Science and Technology（南京理工大学）
Kai Chen, Zhongrui Lin, Jian Wan, Lei Xu, Chungen Xu
备注：
The 18th International Conference on Cryptology and Network Security. Fixed minor issues with the conference version, such as spelling errors and ambiguities in the content description
Searchable symmetric encryption (SSE) for multi-owner model draws much attention as it enables data users to perform searches over encrypted cloud data outsourced by data owners. However, implementing secure and precise query, efficient search and flexible dynamic system maintenance at the same time in SSE remains a challenge. To address this, this paper proposes secure and efficient multi-keyword ranked search over encrypted cloud data for multi-owner model based on searching adversarial networks. We exploit searching adversarial networks to achieve optimal pseudo-keyword padding, and obtain the optimal game equilibrium for query precision and privacy protection strength. Maximum likelihood search balanced tree is generated by probabilistic learning, which achieves efficient search and brings the computational complexity close to $\mathcal{O}(\log N)$. In addition, we enable flexible dynamic system maintenance with balanced index forest that makes full use of distributed computing. Compared with previous works, our solution maintains query precision above 95% while ensuring adequate privacy protection, and introduces low overhead on computation, communication and storage.
Content-based image retrieval (CBIR) is one of the fundamental image retrieval primitives. Its applications can be found in various areas, such as art collections and medical diagnoses. With an increasing prevalence of cloud computing paradigm, image owners desire to outsource their images to cloud servers. In order to deal with the risk of privacy leakage of images, images are typically encrypted before they are outsourced to the cloud, which makes CBIR an extremely challenging task. Existing studies focus on the scenario with only a single image owner, leaving the problem of CBIR with multip…
Performing searches over encrypted data is a very current and active area. Several efficient solutions have been provided for the single-writer scenario in which all sensitive data originates with one party (the Data Owner) that encrypts it and uploads it to a public repository. Subsequently the Data Owner (or authorized clients, the Query Sources) accesses the encrypted data through a Query Processor which has direct access to the public encrypted repository. Motivated by the recent trend in pervasive data collection, we depart from this model and consider a multi-writer scenario in which dat…
Searchable symmetric encryption (SSE) allows the data owner to outsource an encrypted database to a remote server in a private manner while maintaining the ability for selectively search. So far, most existing solutions focus on an honest-but-curious server, while security designs against a malicious server have not drawn enough attention. A few recent works have attempted to construct verifiable SSE that enables the data owner to verify the integrity of search results. Nevertheless, these verification mechanisms are highly dependent on specific SSE schemes, and fail to support complex queries…
Watermarking data for source tracking applications by its owner can be unfair for recipients because the data owner may redistribute the same watermarked data to many users. Hence, each data recipient should know the watermark embedded in their data; however, this may enable them to remove it, which violates the watermark security. To overcome this problem, this research develops a framework that allows the cloud to watermark numerical data taking into consideration: the correctness of the results of selected statistics, data privacy, the recipient's right to know the watermark that is embedde…
Encrypted data deduplication is an important technique for saving storage space and network bandwidth, which has been widely used in cloud storage. Recently, a number of schemes that solve the problem of data deduplication with dynamic ownership management have been proposed. However, these schemes suffer from low efficiency when the dynamic ownership changes a lot. To this end, in this paper, we propose a novel server-side deduplication scheme for encrypted data in a hybrid cloud architecture, where a public cloud (Pub-CSP) manages the storage and a private cloud (Pri-CSP) plays a role as the…
Constrained shortest distance (CSD) querying is one of the fundamental graph query primitives, which finds the shortest distance from an origin to a destination in a graph with a constraint that the total cost does not exceed a given threshold. CSD querying has a wide range of applications, such as routing in telecommunications and transportation. With an increasing prevalence of cloud computing paradigm, graph owners desire to outsource their graphs to cloud servers. In order to protect sensitive information, these graphs are usually encrypted before being outsourced to the cloud. This, howev…
Data is the central asset of today's dynamically operating organization and their business. This data is usually stored in database. A major consideration is applied on the security of that data from the unauthorized access and intruders. Data encryption is a strong option for security of data in database and especially in those organizations where security risks are high. But there is a potential disadvantage of performance degradation. When we apply encryption on database then we should compromise between the security and efficient query processing. The work of this paper tries to fill this …
Cloud computing has become a potential resource for businesses and individuals to outsource their data to remote but highly accessible servers. However, potentials of the cloud services have not been fully unleashed due to users' concerns about security and privacy of their data in the cloud. User-side encryption techniques can be employed to mitigate the security concerns. Nonetheless, once the data in encrypted, no processing (e.g., searching) can be performed on the outsourced data. Searchable Encryption (SE) techniques have been widely studied to enable searching on the data while they are…
Computationally demanding tasks are typically calculated in dedicated data centers, and real-time visualizations also follow this trend. Some rendering tasks, however, require the highest level of confidentiality so that no other party, besides the owner, can read or see the sensitive data. Here we present a direct volume rendering approach that performs volume rendering directly on encrypted volume data by using the homomorphic Paillier encryption algorithm. This approach ensures that the volume data and rendered image are uninterpretable to the rendering server. Our volume rendering pipeline…
This paper presents Droplet, a decentralized data access control service. Droplet enables data owners to securely and selectively share their encrypted data while guaranteeing data confidentiality in the presence of unauthorized parties and compromised data servers. Droplet's contribution lies in coupling two key ideas: (i) a cryptographically-enforced access control construction for encrypted data streams which enables users to define fine-grained stream-specific access policies, and (ii) a decentralized authorization service that serves user-defined access policies. In this paper, we present…
Cloud storage is a widely utilized service for both personal and enterprise demands. However, despite its advantages, many potential users with enormous amounts of sensitive data (big data) refrain from fully utilizing the cloud storage service due to valid concerns about data privacy. An established solution to the cloud data privacy problem is to perform encryption on the client-end. This approach, however, restricts data processing capabilities (eg, searching over the data). Accordingly, the research problem we investigate is how to enable real-time searching over the encrypted big data in …
The problem we address is the following: how can a user employ a predictive model that is held by a third party, without compromising private information. For example, a hospital may wish to use a cloud service to predict the readmission risk of a patient. However, due to regulations, the patient's medical files cannot be revealed. The goal is to make an inference using the model, without jeopardizing the accuracy of the prediction or the privacy of the data. To achieve high accuracy, we use neural networks, which have been shown to outperform other learning models for many tasks. To achieve t…
The increasing popularity of specialized Internet-connected devices and appliances, dubbed the Internet-of-Things (IoT), promises both new conveniences and new privacy concerns. Unlike traditional web browsers, many IoT devices have always-on sensors that constantly monitor fine-grained details of users' physical environments and influence the devices' network communications. Passive network observers, such as Internet service providers, could potentially analyze IoT network traffic to infer sensitive details about users. Here, we examine four IoT smart home devices (a Sense sleep monitor, a N…
Phrase search allows retrieval of documents containing an exact phrase, which plays an important role in many machine learning applications for cloud-based IoT, such as intelligent medical data analytics. In order to protect sensitive information from being leaked by service providers, documents (e.g., clinic records) are usually encrypted by data owners before being outsourced to the cloud. This, however, makes the search operation an extremely challenging task. Existing searchable encryption schemes for multi-keyword search operations fail to perform phrase search, as they are unable to dete…
In this paper, we propose a very secure and reliable owner-self-managed private key recovery method. In recent years, Public Key Authentication (PKA) method has been identified as the most feasible online security solution. However, losing the private key also implies the risk of losing the ownership of the assets associated with the private key. For key protection, the commonly adopted something-you-x solutions require a new secret to protect the target secret and fall into a circular protection issue as the new secret has to be protected too. To resolve the circular protection issue and prov…
Nowadays, a large amount of user privacy-sensitive data is outsourced to the cloud server in ciphertext, which is provided by the data owners and can be accessed by authorized data users. When accessing data, the user should be assigned with the access permission according to his identities or attributes. In addition, the search capabilities in encrypted outsourced data is expected to be enhanced, i.e., the search results can better pre-sent user's intentions. To address the above issues, ESAS, an Efficient Semantic and Authorized Search scheme over encrypt-ed outsourced data, is proposed. In …
A non-fungible token (NFT) references a data store location, typically, using a URL or another unique identifier. At the minimum, a NFT is expected to guarantee ownership and control over the tokenised asset. However, information stored on a third party data store may be copied and stolen. We propose a solution to give control back to the information owner by storing encrypted content on the data store and providing additional security against hacks and zero day exploits. The content on our data store is never decrypted or returned to its owner for decryption during rekeying. Also, the key siz…
We exploit edit distance to quantify keywords similarity and develop two advanced techniques on constructing fuzzy keyword sets, which achieve optimized storage and representation overheads. We further propose a brand new symbol-based trie-traverse searching scheme, where a multi-way tree structure is built up using symbols transformed from the resulted fuzzy keyword sets. Through rigorous security analysis, we show that our proposed solution is secure and privacy-preserving, while correctly realizing the goal of fuzzy keyword search. Extensive experimental results demonstrate the efficiency o…
In this letter we propose Meta-key, a data-sharing mechanism that enables users share their encrypted data under a blockchain-based decentralized storage architecture. All the data-encryption keys are encrypted by the owner's public key and put onto the blockchain for safe and secure storage and easy key-management. Encrypted data are stored in dedicated storage nodes and proxy re-encryption mechanism is used to ensure secure data-sharing in the untrusted environment. Security analysis of our model shows that the proxy re-encryption adopted in our system is naturally free from collusion-attack…
As an important type of cloud data, digital provenance is arousing increasing attention on improving system performance. Currently, provenance has been employed to provide cues regarding access control and to estimate data quality. However, provenance itself might also be sensitive information. Therefore, provenance might be encrypted and stored in the Cloud. In this paper, we provide a mechanism to classify cloud documents by searching specific keywords from their encrypted provenance, and we prove our scheme achieves semantic security. In term of application of the proposed techniques, consi…
Rochester Institute of Technology（罗切斯特理工学院）；Taif University（塔伊夫大学）
Homomorphic encryption (HE) is a promising cryptographic technique for enabling secure collaborative machine learning in the cloud. However, support for homomorphic computation on ciphertexts under multiple keys is inefficient. Current solutions often require key setup before any computation or incur large ciphertext size (at best, grow linearly to the number of involved keys). In this paper, we proposed a new approach that leverages threshold and multi-key HE to support computations on ciphertexts under different keys. Our new approach removes the need for key setup between each client and th…
With the rapid increase in cloud computing, concerns surrounding data privacy, security, and confidentiality also have been increased significantly. Not only cloud providers are susceptible to internal and external hacks, but also in some scenarios, data owners cannot outsource the computation due to privacy laws such as GDPR, HIPAA, or CCPA. Fully Homomorphic Encryption (FHE) is a groundbreaking invention in cryptography that, unlike traditional cryptosystems, enables computation on encrypted data without ever decrypting it. However, the most critical obstacle in deploying FHE at large-scale …
Database users have started moving toward the use of cloud computing as a service because it provides computation and storage needs at affordable prices. However, for most of the users, the concern of privacy plays a major role as they cannot control data access once their data are outsourced, especially if the cloud provider is curious about their data. Data encryption is an effective way to solve privacy concerns, but executing queries over encrypted data is a problem that needs attention. In this research, we introduce a bit-based model to execute different relational algebra operators over…
In this paper, we have introduced a new type of leakage associated with modern encrypted databases called update pattern leakage. We formalize the definition and security model of DP-Sync with DP update patterns. We also proposed the framework DP-Sync, which extends existing encrypted database schemes to DP-Sync with DP update patterns. DP-Sync guarantees that the entire data update history over the outsourced data structure is protected by differential privacy. This is achieved by imposing differentially-private strategies that dictate the data owner's synchronization of local~data.
Important document are being kept encrypted in remote servers. In order to retrieve these encrypted data, efficient search methods needed to enable the retrieval of the document without knowing the content of the documents In this paper a technique called a global heuristic search on encrypted data (GHSED) technique will be described for search in an encrypted files using public key encryption stored on an untrusted server and retrieve the files that satisfy a certain search pattern without revealing any information about the original files. GHSED technique would satisfy the following: (1) Pro…
PSL University（巴黎高等师范学院（PSL大学））；ENS（巴黎高等师范学院）；INRIA（法国国家信息与自动化研究所）；University of California, Berkeley（加州大学伯克利分校）；CNRS（法国国家科学研究中心）；Département d’informatique de l’ENS（巴黎高等师范学院计算机系）
Machine learning on encrypted data has received a lot of attention thanks to recent breakthroughs in homomorphic encryption and secure multi-party computation. It allows outsourcing computation to untrusted servers without sacrificing privacy of sensitive data. We propose a practical framework to perform partially encrypted and privacy-preserving predictions which combines adversarial training and functional encryption. We first present a new functional encryption scheme to efficiently compute quadratic functions so that the data owner controls what can be computed but is not involved in the c…
IBM Research AI（IBM研究院AI部门）；Mohamed Bin Zayed University of AI（穆罕默德·本·扎耶德人工智能大学）；University at Buffalo, SUNY（纽约州立大学布法罗分校）；Microsoft Research（微软研究院）
Data privacy concerns often prevent the use of cloud-based machine learning services for sensitive personal data. While homomorphic encryption (HE) offers a potential solution by enabling computations on encrypted data, the challenge is to obtain accurate machine learning models that work within the multiplicative depth constraints of a leveled HE scheme. Existing approaches for encrypted inference either make ad-hoc simplifications to a pre-trained model (e.g., replace hard comparisons in a decision tree with soft comparators) at the cost of accuracy or directly train a new depth-constrained …
We present a method to search for a probe (or query) image representation against a large gallery in the encrypted domain. We require that the probe and gallery images be represented in terms of a fixed-length representation, which is typical for representations obtained from learned networks. Our encryption scheme is agnostic to how the fixed-length representation is obtained and can therefore be applied to any fixed-length representation in any application domain. Our method, dubbed HERS (Homomorphically Encrypted Representation Search), operates by (i) compressing the representation towards…
In the use of a cloud storage, sharing of data with efficient access control is an important requirement in addition to data security and privacy. Cui et al. (IEEE Trans. on Comp. 2016) proposed \textit{key-aggregate searchable encryption (KASE)}, which allows a data owner to issue an \textit{aggregate key} that enables a user to search in an authorized subset of encrypted files by generating an encrypted keyword called \textit{trapdoor}. While the idea of KASE is elegant, to the best of our knowledge, its security has never been discussed formally. In this paper, we discuss the security of KA…
University of Central Florida（中佛罗里达大学）；Kent State University（肯特州立大学）；Texas Tech University（得克萨斯理工大学）；University of Houston（休斯顿大学）；Drexel University（德雷塞尔大学）
Performing neural network inference on encrypted data without decryption is one popular method to enable privacy-preserving neural networks (PNet) as a service. Compared with regular neural networks deployed for machine-learning-as-a-service, PNet requires additional encoding, e.g., quantized-precision numbers, and polynomial activation. Encrypted input also introduces novel challenges such as adversarial robustness and security. To the best of our knowledge, we are the first to study questions including (i) Whether PNet is more robust against adversarial inputs than regular neural networks? (…
Link Prediction is an important and well-studied problem for social networks. Given a snapshot of a graph, the link prediction problem predicts which new interactions between members are most likely to occur in the near future. As networks grow in size, data owners are forced to store the data in remote cloud servers which reveals sensitive information about the network. The graphs are therefore stored in encrypted form. We study the link prediction problem on encrypted graphs. To the best of our knowledge, this secure link prediction problem has not been studied before. We use the number of c…
When working with joint collections of confidential data from multiple sources, e.g., in cloud-based multi-party computation scenarios, the ownership relation between data providers and their inputs itself is confidential information. Protecting data providers' privacy desires a function for secretly shuffling the data collection. We present the first efficient secure multi-party computation protocol for secret shuffling in scenarios with a central server. Based on a novel approach to random index distribution, our solution enables the randomization of the order of a sequence of encrypted data…
In recent years, with the development of cloud computing platforms, privacy-preserving methods for deep learning have become an urgent problem. NeuraCrypt is a private random neural network for privacy-preserving that allows data owners to encrypt the medical data before the data uploading, and data owners can train and then test their models in a cloud server with the encrypted data directly. However, we point out that the performance of NeuraCrypt is heavily degraded when using color images. In this paper, we propose a Color-NeuraCrypt to solve this problem. Experiment results show that our …
A symmetric searchable encryption (SSE) scheme allows a client (data owner) to search on encrypted data outsourced to an untrusted cloud server. The search may either be a single keyword search or a complex query search like conjunctive or Boolean keyword search. Information leakage is quite high for dynamic SSE, where data might be updated. It has been proven that to avoid this information leakage an SSE scheme with dynamic data must be forward private. A dynamic SSE scheme is said to be forward private, if adding a keyword-document pair does not reveal any information about the previous sear…
Adoption of artificial intelligence medical imaging applications is often impeded by barriers between healthcare systems and algorithm developers given that access to both private patient data and commercial model IP is important to perform pre-deployment evaluation. This work investigates a framework for secure, privacy-preserving and AI-enabled medical imaging inference using CrypTFlow2, a state-of-the-art end-to-end compiler allowing cryptographically secure 2-party Computation (2PC) protocols between the machine learning model vendor and target patient data owner. A common DenseNet-121 che…
In this paper, we present a comprehensive architecture for confidential computing, which we show to be general purpose and quite efficient. It executes the application as is, without any added burden or discipline requirements from the application developers. Furthermore, it does not require the trust of system software at the computing server and does not impose any added burden on the communication subsystem. The proposed Encrypted Data Processing (EDAP) architecture accomplishes confidentiality, authenticity, and freshness of the key-based cryptographic data protection by adopting data encr…
Order-preserving encryption allows encrypting data, while still enabling efficient range queries on the encrypted data. Moreover, it does not require any change to the database management system, because comparison operates on ciphertexts as on plaintexts. This makes order-preserving encryption schemes very suitable for data outsourcing in cloud computing scenarios. However, all order-preserving encryption schemes are necessarily symmetric limiting the use case to one client and one server. Imagine a scenario where a Data Owner encrypts its data before outsourcing it to the Cloud Service Provi…
Cloud service providers offer a low-cost and convenient solution to host unstructured data. However, cloud services act as third-party solutions and do not provide control of the data to users. This has raised security and privacy concerns for many organizations (users) with sensitive data to utilize cloud-based solutions. User-side encryption can potentially address these concerns by establishing user-centric cloud services and granting data control to the user. Nonetheless, user-side encryption limits the ability to process (e.g., search) encrypted data on the cloud. Accordingly, in this res…
With the increasing affordability and availability of patient data, hospitals tend to outsource their data to cloud service providers (CSPs) for the purpose of storage and analytics. However, the concern of data privacy significantly limits the data owners' choice. In this work, we propose the first solution, to the best of our knowledge, that allows a CSP to perform efficient identification of target patients (e.g., pre-processing for a genome-wide association study - GWAS) over multi-tenant encrypted phenotype data (owned by multiple hospitals or data owners). We first propose an encryption …
The ongoing trend of moving data and computation to the cloud is met with concerns regarding privacy and protection of intellectual property. Cloud Service Providers (CSP) must be fully trusted to not tamper with or disclose processed data, hampering adoption of cloud services for many sensitive or critical applications. As a result, CSPs and CPU manufacturers are rushing to find solutions for secure outsourced computation in the Cloud. While enclaves, like Intel SGX, are strongly limited in terms of throughput and size, AMD's Secure Encrypted Virtualization (SEV) offers hardware support for t…
User-generated data is crucial to predictive modeling in many applications. With a web/mobile/wearable interface, a data owner can continuously record data generated by distributed users and build various predictive models from the data to improve their operations, services, and revenue. Due to the large size and evolving nature of users data, data owners may rely on public cloud service providers (Cloud) for storage and computation scalability. Exposing sensitive user-generated data and advanced analytic models to Cloud raises privacy concerns. We present a confidential learning framework, Se…
Cloud-based enterprise search services (e.g., Amazon Kendra) are enchanting to big data owners by providing them with convenient search solutions over their enterprise big datasets. However, individuals and businesses that deal with confidential big data (eg, credential documents) are reluctant to fully embrace such services, due to valid concerns about data privacy. Solutions based on client-side encryption have been explored to mitigate privacy concerns. Nonetheless, such solutions hinder data processing, specifically clustering, which is pivotal in dealing with different forms of big data. …
Considerable attention has been paid to dynamic searchable symmetric encryption (DSSE) which allows users to search on dynamically updated encrypted databases. To improve the performance of real-world applications, recent non-interactive multi-client DSSE schemes are targeted at avoiding per-query interaction between data owners and data users. However, existing non-interactive multi-client DSSE schemes do not consider forward privacy or backward privacy, making them exposed to leakage abuse attacks. Besides, most existing DSSE schemes with forward and backward privacy rely on keeping a keywor…
Healthcare data in cloud computing facilitates the treatment of patients efficiently by sharing information about personal health data between the healthcare providers for medical consultation. Furthermore, retaining the confidentiality of data and patients' identity is a another challenging task. This paper presents the concept of an access control-based (AC) privacy preservation model for the mutual authentication of users and data owners in the proposed digital system. The proposed model offers a high-security guarantee and high efficiency. The proposed digital system consists of four diffe…
