.png){kind=icon}
.png){kind=icon}
Return
Improving Privacy in Data Service Composition
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2021.3094188.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Today, the de-facto standard mechanism for data service providers to share their services is through web-service interfaces; clients invoke the service through request messages and receive the data as payloads in the corresponding response messages. Typically, clients need information beyond what any single provider offers; in such cases, multiple data services must be composed to provide a complete solution meeting the client needs. The term data-service composition refers to a unified interface that delivers to the client the data it needs in response to a single request message, as if it were available withing a single source. Data-service composition is useful and convenient for the requesting client, but raises privacy concerns since a participating data-service provider potentially can infer information about the data held by other providers. In this paper, we propose a data-service composition method that relies on a mediator for the communication between any two service providers, ensures that the mediator is strictly following the data-composition plan, and maintains privacy between the mediator and service providers. The data service provider first authenticates that the input data is coming from the correct source as per the composition plan, and this is done whilst ensuring complete privacy between the mediator and other service providers. Similarly, data service providers also authenticate the destination of their output data. The approach is validated and its performance evaluated using a real world online retail dataset.
Keywords:
Data privacy
Privacy
Cryptography
Databases
Web services
Task analysis
Licenses
Web services
privacy-preserving service composition
privacy-preserving web data integration
privacy in data service composition
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W