Improved outsourced private set intersection protocol based on polynomial interpolation

Yang, Xiaoyuan; Xue-shan, Luo; Wang, Xu An; Zhang, Shuaiwei

doi:10.1002/cpe.4329

Cited by 17 publications

(10 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e data user and the data owner would like to outsource their data sets to the cloud and delegate set intersection computation to the cloud. [5,7,11]. Moreover, public key encryption with equality test [12][13][14][15] can also be used to attain this goal.…”

Section: Related Workmentioning

confidence: 99%

Delegated Key-Policy Attribute-Based Set Intersection over Outsourced Encrypted Data Sets for CloudIoT

Shi

Qiu

2021

Security and Communication Networks

View full text Add to dashboard Cite

Private set intersection (PSI) is a fundamental cryptographic primitive, allowing two parties to calculate the intersection of their data sets without exposing additional private information. In cloud-based IoT system, IoT-enabled devices would like to outsource their data sets in their encrypted form to the cloud. In this scenario, how to delegate the set intersection computation over outsourced encrypted data sets to the cloud and how to achieve the fine-grained access control for PSI without divulging any additional information to the cloud are still open problems. With that in mind, in this work, we combine key-policy attribute-based encryption (KP-ABE) and PSI to introduce such a novel concept, called delegated key-policy attribute-based set intersection over outsourced encrypted data sets (KP-ABSI), to solve this problem. Then we propose a first concrete KP-ABSI scheme and analyze its efficiency.

show abstract

Section: Related Workmentioning

confidence: 99%

Delegated Key-Policy Attribute-Based Set Intersection over Outsourced Encrypted Data Sets for CloudIoT

Shi

Qiu

2021

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…Supposing the smaller set with the size N s and the larger one with N l , the communication complexity of the proposed scheme is O(N s log N l ). With the multiplicatively homomorphic property of RSA cryptosystem, Yang et al [167] presented an efficient set intersection protocol, secure in the semi-honest model. The protocol assumes that two different parties (i.e., A and B) hold their individual private sets, which are encrypted and outsourced to the cloud.…”

Section: ) Set Intersectionmentioning

confidence: 99%

“…A well-constructed scheme is not supposed to disturb users nor add excessive burden to servers. To measure the efficiency of related works [167], [170], [171], [179], [182], [183], we consider the overhead of the involved operations (including the time and resources consumed for data encryption/decryption, data processing, data transmission and result verification), which are generalized as computation and communication complexities. Take the scheme [170] and [171] as examples to analyze the complexities of their private set union protocols.…”

Section: Computation and Communication Complexitymentioning

confidence: 99%

“…The interactions generally occur between the client and the server (or between the servers). Many HE-based outsourcing schemes [152], [167], [171], [208], [245], [263], [291] involve one single interaction between the client and the server, which means the client only needs to send his encrypted computational request to the server and waits for the server to return the computational result. The computation processes are entirely performed by the server, thereby minimizing the client's burden.…”

Section: Interactionmentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Survey on Secure Outsourced Computation and Its Applications

et al. 2019

View full text Add to dashboard Cite

With the ever-increasing requirement of storage and computation resources, it is unrealistic for local devices (with limited sources) to implement large-scale data processing. Therefore, individuals or corporations incline to outsource their computation requirements to the cloud. However, data outsourcing brings security and privacy concerns to users when the cloud servers are not fully trusted. Recently, extensive research works are conducted, aiming at secure outsourcing schemes for diverse computational tasks via different technologies. In this survey, we provide a technical review and comparison of existing outsourcing schemes using diverse secure computation methods. Specifically, we begin the survey by describing security threats and requirements of secure outsourcing computation. Meanwhile, we introduce four secure techniques (i.e., secure multi-party computation, pseudorandom functions, software guard extensions, and perturbation approaches) and their related works. Then, we focus on the theories and evolution of homomorphic encryption, as well as the applications of the basic operations and application-specific tasks. Finally, we discuss the security and performance of existing works and give future directions in this field. INDEX TERMS Secure outsourced computing, privacy preserving, homomorphic encryption, secure outsourced machine learning, data processing.

show abstract

“…The last paper “ Improved Outsourced Private Set Intersection Protocol Based on Polynomial Interpolation ” by Yang et al investigates issues in private set intersection (PSI) protocols, which enable two parties to compute the intersection of their inputs without compromising anything about the datasets beyond the intersection. The authors present a variant of delegated private set intersection protocol secure in the semi‐honest model under RSA assumption as well as an efficient and secure outsourcing computation algorithm for RSA crypto‐system.…”

mentioning

confidence: 99%

Special issue on advanced techniques for cloud data storage and collaborative systems

Xhafa

2017

Concurrency and Computation

View full text Add to dashboard Cite

Cloud computing paradigm builds on the foundations of distributed computing, grid computing, virtualisation, service orientation, etc. Cloud storage, which is one of the most attractive cloud services, offers numerous benefits from both the technology and functionality perspectives such as increased availability, flexibility, and functionality. Therefore, Cloud-based platforms have become fundamental to collaborative and data management systems. However, the new Cloud environment creates major collaborative and data management challenges such as efficient and reliable remote storage, online data query processing, data integrity, outsourcing computation, and secure virtualisation, among others. Such issues need to be carefully studied and solved to enable the wide deployment and adoption of Cloud computing in businesses, industry, etc.The purpose of this special issue is to publish recent advances in Cloud-based collaborative systems and Cloud storage. The special issue comprises five high-quality papers, which are arranged as follows. and validate the applicability of the proposed approaches to the automatic annotation of business service descriptions to service concepts on a real-world dataset.In the third paper, 3 "Sensor Data Management in the Cloud: Data Storage, Data Ingestion, and Data Retrieval," the authors address an increasing need to capture, store, and analyse the dynamic semistructured data from sensors. A similar growth of semistructured data in the modern Web has led to the creation of NoSQL data stores for scalability, availability, and performance, whereas large-scale data processing frameworks for parallel analysis.The authors study how sensor data management can benefit from MongoDB with Apache Spark technologies, specifically for ingesting high-velocity sensor data and parallel retrieval of high volume data. The performance of MongoDB sharding and no-sharding databases with Apache Spark are evaluated to identify the right software environment for sensor data management. Park et al in the fourth paper, 4 "Study on the SDN-IP based Solution of Well-known Bottleneck Problems in Private Sector of National R&E Network for BigData Transfer," deal with issues arising in Software Defined Networking (SDN). The key feature of SDN networks is the provisioning of dynamic service architecture. Therefore, it is more appropriate for the big data science computing trends, for handling today's big data science, which requires massive parallel processing and a constant demand for additional capacity and on demand connectivity. The authors bring the experience from KREONET (Korea Research Environment Open Network) and Korea National R&E Network, which adopt SDN as a platform service for big data scientific application. The big data application for KREONET SDN service comprises high-energy physics, astrophysics, biology/genetics, meteorology, artificial satellite data, etc.The last paper 5 "Improved Outsourced Private Set Intersection Protocol Based on Polynomial Interpolation" by Yang et al investigates issues ...

show abstract

Improved outsourced private set intersection protocol based on polynomial interpolation

Cited by 17 publications

References 42 publications

Delegated Key-Policy Attribute-Based Set Intersection over Outsourced Encrypted Data Sets for CloudIoT

Delegated Key-Policy Attribute-Based Set Intersection over Outsourced Encrypted Data Sets for CloudIoT

A Comprehensive Survey on Secure Outsourced Computation and Its Applications

Special issue on advanced techniques for cloud data storage and collaborative systems

Contact Info

Product

Resources

About