Dataset Information

Improving the proof of "Privacy-preserving attribute-keyword based data publish-subscribe service on cloud platforms".

ABSTRACT: Most recently, Kan Yang et al. proposed an attribute-keyword based encryption scheme for data publish-subscribe service(AKPS), which is highly useful for cloud storage scenario. Unfortunately, we discover that there is a flaw in the security proof of indistinguishability of the tag and trapdoor against chosen keyword attack under the Bilinear Diffie-Hellman (BDH) assumption. As the security proof is a key component for a cryptographic scheme, based on the Decisional Diffie-Hellman (DDH) assumption, we improve the security proof method and give a new security proof of the AKPS scheme for indistinguishability of the tag and trapdoor in our proposal, which is more rigorous than the original one. Furthermore, we also demonstrate that the AKPS scheme is secure against data Replayable Chosen Ciphertext Attack (RCCA).

SUBMITTER: Wang S

PROVIDER: S-EPMC6388936 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

Improving the proof of "Privacy-preserving attribute-keyword based data publish-subscribe service on cloud platforms".

Wang Shangping S Zhang Qian Q Zhang Yaling Y Sun Jin J Chen Juanjuan J Sun Xiaoqing X

PloS one 20190225 2

Most recently, Kan Yang et al. proposed an attribute-keyword based encryption scheme for data publish-subscribe service(AKPS), which is highly useful for cloud storage scenario. Unfortunately, we discover that there is a flaw in the security proof of indistinguishability of the tag and trapdoor against chosen keyword attack under the Bilinear Diffie-Hellman (BDH) assumption. As the security proof is a key component for a cryptographic scheme, based on the Decisional Diffie-Hellman (DDH) assumpti ...[more]

PMID: 30802275

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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.

Dataset Information

Improving the proof of "Privacy-preserving attribute-keyword based data publish-subscribe service on cloud platforms".

Publications

Improving the proof of "Privacy-preserving attribute-keyword based data publish-subscribe service on cloud platforms".

Similar Datasets

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