Improving the proof of "Privacy-preserving attribute-keyword based data publish-subscribe service on cloud platforms".
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ABSTRACT:
SUBMITTER: Wang S
PROVIDER: S-EPMC6388936 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Ciphertext-policy attribute-based encryption (CP-ABE) scheme is a new type of data encryption primitive, which is very suitable for data cloud storage for its fine-grained access control. Keyword-based searchable encryption scheme enables users to quickly find interesting data stored in the cloud server without revealing any information of the searched keywords. In this work, we provide a keyword searchable attribute-based encryption scheme with attribute update for cloud storage, which is a combination of attribute-based encryption scheme and keyword searchable encryption scheme. The new scheme supports the user's attribute update, especially in our new scheme when a user's attribute need to be updated, only the user's secret key related with the attribute need to be updated, while other user's secret key and the ciphertexts related with this attribute need not to be updated with the help of the cloud server. In addition, we outsource the operation with high computation cost to cloud server to reduce the user's computational burden. Moreover, our scheme is proven to be semantic security against chosen ciphertext-policy and chosen plaintext attack in the general bilinear group model. And our scheme is also proven to be semantic security against chosen keyword attack under bilinear Diffie-Hellman (BDH) assumption.
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Project description:We propose an attribute-based encryption scheme with multi-keyword search and supporting attribute revocation in cloud storage environment, in which binary attributes and AND-gate access policy are used. Our proposal enjoys several advantages. Firstly, multi-keyword search is available, and only when a data user's attribute set satisfies access policy in keyword index, and keyword token generated by data user matches index successfully, then data user can obtain ciphertext containing keywords. In this way, more accurate keyword search is achievable. Secondly, the search privacy of data user is protected owing to cloud servers cannot obtain any knowledge of keywords which data user is interested in. Meanwhile, the ciphertext is able to be decrypted when data user's attribute set satisfies access policy specified in the ciphertext, which can both improve security of encryption and achieve secure fine-grained access control. Thirdly, the proposed scheme supports attribute revocation, in our scheme when a data user's attribute is revoked, the version number of attribute, non-revoked data users' secret keys and related ciphertexts will be updated, such that data user whose attribute is revoked does not decrypt updated ciphertext anymore. In addition, based on the assumption of decisional linear (DL) and decisional Diffie-Hellman (DDH), our scheme is proved to be secure against selectively chosen-keyword attacks and selectively chosen-plaintext attacks respectively, and it also ensures token privacy security.
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Project description:Digital security as a service is a crucial aspect as it deals with user privacy provision and secure content delivery to legitimate users. Most social media platforms utilize end-to-end encryption as a significant security feature. However, multimedia data transmission in group communication is not encrypted. One of the most important objectives for a service provider is to send the desired multimedia data/service to only legitimate subscriber. Broadcast encryption is the most appropriate cryptographic primitive solution for this problem. Therefore, this study devised a construction called anonymous revocable identity-based broadcast encryption that preserves the privacy of messages broadcasted and the identity of legitimate users, where even revoked users cannot extract information about the user's identity and sent data. The update key is broadcast periodically to non-revoked users, who can obtain the message using the update and decryption keys. A third-party can also revoke the users. It is proven that the proposed construction is semantically secure against IND-ID-CPA attacks and efficient in terms of computational cost and communication bandwidth.
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Project description:With the development of cloud computing, interest in database outsourcing has recently increased. In cloud computing, it is necessary to protect the sensitive information of data owners and authorized users. For this, data mining techniques over encrypted data have been studied to protect the original database, user queries and data access patterns. The typical data mining technique is kNN classification which is widely used for data analysis and artificial intelligence. However, existing works do not provide a sufficient level of efficiency for a large amount of encrypted data. To solve this problem, in this paper, we propose a privacy-preserving parallel kNN classification algorithm. To reduce the computation cost for encryption, we propose an improved secure protocol by using an encrypted random value pool. To reduce the query processing time, we not only design a parallel algorithm, but also adopt a garbled circuit. In addition, the security analysis of the proposed algorithm is performed to prove its data protection, query protection, and access pattern protection. Through our performance evaluation, the proposed algorithm shows about 2∼25 times better performance compared with existing algorithms.
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Project description:Secure group communication in Vehicle Ad hoc Networks (VANETs) over open channels remains a challenging task. To enable secure group communications with conditional privacy, it is necessary to establish a secure session using Authenticated Key Agreement (AKA). However, existing AKAs suffer from problems such as cross-domain dynamic group session key negotiation and heavy computational burdens on the Trusted Authority (TA) and vehicles. To address these challenges, we propose a dynamic privacy-preserving anonymous authentication scheme for condition matching in fog-cloud-based VANETs. The scheme employs general Elliptic Curve Cryptosystem (ECC) technology and fog-cloud computing methods to decrease computational overhead for On-Board Units (OBUs) and supports multiple TAs for improved service quality and robustness. Furthermore, certificateless technology alleviates TAs of key management burdens. The security analysis indicates that our solution satisfies the communication security and privacy requirements. Experimental simulations verify that our method achieves optimal overall performance with lower computational costs and smaller communication overhead compared to state-of-the-art solutions.
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Project description:With the development of information technology and the Internet, users can conveniently use roaming services without time and space restrictions. This roaming service is initiated by establishing a session key between a home node, which exists in a home network, and a mobile node, which exists in a foreign network. However, in the process of verifying a legitimate user and establishing a session key, various security threats and privacy exposure issues can arise. This study demonstrates that the authentication scheme for the roaming service proposed in the existing Global Mobility Network (GLOMONET) environment has several vulnerabilities and, hence, is impractical. In addition, the scheme does not satisfy the privacy of the session key or user's identity or password. Accordingly, we propose a new lightweight authentication scheme to compensate for these vulnerabilities and secure a high level of privacy, such as non-traceability. In addition, formal and informal analyses are conducted to examine the safety of the proposed scheme. Based on the results of our analyses, we prove that the proposed scheme is highly secure and applicable to the actual GLOMONET environment.
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Project description:BackgroundThe protection of private data is a key responsibility for research studies that collect identifiable information from study participants. Limiting the scope of data collection and preventing secondary use of the data are effective strategies for managing these risks. An ideal framework for data collection would incorporate feature engineering, a process where secondary features are derived from sensitive raw data in a secure environment without a trusted third party.ObjectiveThis study aimed to compare current approaches based on how they maintain data privacy and the practicality of their implementations. These approaches include traditional approaches that rely on trusted third parties, and cryptographic, secure hardware, and blockchain-based techniques.MethodsA set of properties were defined for evaluating each approach. A qualitative comparison was presented based on these properties. The evaluation of each approach was framed with a use case of sharing geolocation data for biomedical research.ResultsWe found that approaches that rely on a trusted third party for preserving participant privacy do not provide sufficiently strong guarantees that sensitive data will not be exposed in modern data ecosystems. Cryptographic techniques incorporate strong privacy-preserving paradigms but are appropriate only for select use cases or are currently limited because of computational complexity. Blockchain smart contracts alone are insufficient to provide data privacy because transactional data are public. Trusted execution environments (TEEs) may have hardware vulnerabilities and lack visibility into how data are processed. Hybrid approaches combining blockchain and cryptographic techniques or blockchain and TEEs provide promising frameworks for privacy preservation. For reference, we provide a software implementation where users can privately share features of their geolocation data using the hybrid approach combining blockchain with TEEs as a supplement.ConclusionsBlockchain technology and smart contracts enable the development of new privacy-preserving feature engineering methods by obviating dependence on trusted parties and providing immutable, auditable data processing workflows. The overlap between blockchain and cryptographic techniques or blockchain and secure hardware technologies are promising fields for addressing important data privacy needs. Hybrid blockchain and TEE frameworks currently provide practical tools for implementing experimental privacy-preserving applications.
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Project description:Attribute based encryption (ABE) is a good way to achieve flexible and secure access control to data, and attribute revocation is the extension of the attribute-based encryption, and the keyword search is an indispensable part for cloud storage. The combination of both has an important application in the cloud storage. In this paper, we construct a searchable attribute-based encryption scheme with attribute revocation in cloud storage, the keyword search in our scheme is attribute based with access control, when the search succeeds, the cloud server returns the corresponding cipher text to user and the user can decrypt the cipher text definitely. Besides, our scheme supports multiple keywords search, which makes the scheme more practical. Under the assumption of decisional bilinear Diffie-Hellman exponent (q-BDHE) and decisional Diffie-Hellman (DDH) in the selective security model, we prove that our scheme is secure.
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Project description:Location-based services (LBS), as one of the most popular location-awareness applications, has been further developed to achieve low-latency with the assistance of fog computing. However, privacy issues remain a research challenge in the context of fog computing. Therefore, in this paper, we present a fine-grained and privacy-preserving query scheme for fog computing-enhanced location-based services, hereafter referred to as FGPQ. In particular, mobile users can obtain the fine-grained searching result satisfying not only the given spatial range but also the searching content. Detailed privacy analysis shows that our proposed scheme indeed achieves the privacy preservation for the LBS provider and mobile users. In addition, extensive performance analyses and experiments demonstrate that the FGPQ scheme can significantly reduce computational and communication overheads and ensure the low-latency, which outperforms existing state-of-the art schemes. Hence, our proposed scheme is more suitable for real-time LBS searching.
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Project description:Cloud manufacturing is a new globalized manufacturing framework which has improved the performance of manufacturing systems. The service-oriented architecture as the main idea behind this framework means that all resources and capabilities are considered as services. The agents interact by way of service exchanging, which has been a part of service composition research topics. Service allocations to demanders in a cloud manufacturing system have a dynamic behavior. However, the current research studies on cloud-based service composition are mainly based on centralized global optimization models. Therefore, a distributed deployment and real-time synchronization platform, which enables the globalized collaboration in service composition, is required. This paper proposes a method of using blockchain to solve these issues. Each service composition is considered as a transaction in the blockchain concept. A block includes a set of service compositions and its validity is confirmed by a predefined consensus mechanism. In the suggested platform, the mining role in blockchain is interpreted as an endeavor for proposing the proper service composition in the manufacturing paradigm. The proposed platform has interesting capabilities as it can increase the response time using the blockchain technology and improve the overall optimality of supply-demand matching in cloud manufacturing. The efficiency of the proposed model was evaluated by investigating a service allocation problem in a cloud manufacturing system in four large scale problems. Each problem is examined in four centralized modes, two, three and four solvers in blockchain-based model. The simulation results indicate the high quality of the proposed solution. The proposed solution will lead to at least 15.14% and a maximum of 34.8 percent reduction in costs and 20 to 68.4 percent at the solving time of the problem. It is also observed that with increasing the number of solvers (especially in problems with larger dimensions) the solution speed increases sharply (more than 68% improvement in some problems), which indicates the positive effect of distribution on reducing the problem-solving time.
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