Privacy in Data Service Composition

Abstract

In modern information systems different information features, about the same individual, are often collected and managed by autonomous data collection services that may have different privacy policies. Answering many end-users' legitimate queries requires the integration of data from multiple such services. However, data integration is often hindered by the lack of a trusted entity, often called a mediator, with which the services can share their data and delegate the enforcement of their privacy policies. In this paper, we propose a flexible privacy-preserving data integration approach for answering data integration queries without the need for a trusted mediator. In our approach, services are allowed to enforce their privacy policies locally. The mediator is considered to be untrusted, and only has access to encrypted information to allow it to link data subjects across the different services. Services, by virtue of a new privacy requirement, dubbed k-Protection, limiting privacy leaks, cannot infer information about the data held by each other. End-users, in turn, have access to privacy-sanitized data only. We evaluated our approach using an example and a real dataset from the healthcare application domain. The results are promising from both the privacy preservation and the performance perspectives.

Improving Privacy in Data Service Composition

“…Intermediate data is shared with the mediator in a secure manner such that the mediator can orchestrate the data composition plan, perform join operations on the collected data without seeing the data. M. Barhamgi et.al [14] do something similar but the big limitation of their work is that the privacy critical attributes need not to be numerical. In this paper, we overcome this limitation and ensure privacy irrespective of the type of the privacy critical attribute.…”

“…In this paper, we overcome this limitation and ensure privacy irrespective of the type of the privacy critical attribute. Like [14], K-Anonymity is used for value generalization to preserve the privacy of data service providers during data service composition. We specifically utilise the "Datasetbased identifier generalization" method discussed in [14] for value generalization using K-Anonymity.…”

“…[14] is the latest in the field of privacy in data service integration. The mediator is not considered trustworthy in the technique proposed by [14]. Data service providers share their data with the mediator in encrypted form.…”

“…al. [14] employ an effective technique of encryption called OPES [15]. OPES encrypts numerical data in such a manner that the order of the encrypted values is preserved.…”

“…al. [14] use OPES for query execution, join operations, and finding K-Anonimity of a value. A major limitation with OPES is that it can only be used to encrypt numerical data.…”