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.

This article describes how data locality is becoming one of the most critical factors to affect performance of MapReduce clusters because of network bisection bandwidth becomes a bottleneck. Task scheduler assigns the most appropriate map tasks to nodes. If map tasks are scheduled to nodes without input data, these tasks will issue remote I/O operations to copy the data to local nodes that decrease execution time of map tasks. In that case, prefetching mechanism can be useful to preload the needed input data before tasks is launching. Therefore, the key challenge is how this article can accurately predict the execution time of map tasks to be able to use data prefetching effectively without any data access delay. In this article, it is proposed that a Predictive Map Task Scheduler assigns the most suitable map tasks to nodes ahead of time. Following this, a linear regression model is used for prediction and data locality based algorithm for tasks scheduling. The experimental results show that the method can greatly improve both data locality and execution time of map tasks.

We propose a service-oriented privacy-preserving model for data integration across autonomous clouds. Our model allows to execute aggregations of cloud data sharing services without revealing any extra information to any of the involved services, i.e., none of involved services nor their providers are able to learn/infer any information about the data the other services provide beyond what these services already know.

In this paper, we tackle privacy issues of data sharing services in a cloud environment. We propose a serviceoriented privacy-preserving model for data integration across autonomous clouds. Our model allows to execute aggregations of cloud data sharing services without revealing any extra information to any of the involved services. Thus, involved services enforce locally their privacy policies by applying their own access control models and data anonymization algorithms. We illustrate lack of data protection in current data sources through examples of querying data in a healthcare system.