Privacy-Preserving Mining of Web Service Conversations

Organizations and businesses are exporting their applications as Web services seeking more collaboration opportunities. These services are generally not used in silos. Indeed, the invocation of a service is often conditioned by the invocation of other services. We refer to the precedence relationships between service invocations as conversations or choreographies. As clients interact with Web services, they exchange an important quantity of sensitive data, hence raising the challenge to keep the privacy of various interactions. In addition to the data exchanged with Web services, users may consider the information about service usage as sensitive and would like to hide that information from third parties. However, conversation relationships may complicate the task of keeping such information secret. In this Thesis, we extend the traditional concept of k-anonymity introduced for databases to Web service conversations. The goal is to determine the extent to which the invocation of a service can be inferred from downstream invocations. We first use the FP-Growth algorithm for mining service invocation logs. The mining process returns the probabilities of service conversations. We then define a probabilistic k-anonymity technique for Web service conversations based on the results of the mining process. The proposed approach assists users in selecting Web services that best satisfy their anonymity requirements. We conducted extensive experiments using realworld Web services to prove the efficiency of the proposed approach.Master of ScienceComputer and Information Science, College of Engineering and Computer ScienceCollege of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/138104/1/Privacy-Preserving Mining of Web Service Conversations.pdfDescription of Privacy-Preserving Mining of Web Service Conversations.pdf : Thesi

Cell fault management using machine learning techniques

This paper surveys the literature relating to the application of machine learning to fault management in cellular networks from an operational perspective. We summarise the main issues as 5G networks evolve, and their implications for fault management. We describe the relevant machine learning techniques through to deep learning, and survey the progress which has been made in their application, based on the building blocks of a typical fault management system. We review recent work to develop the abilities of deep learning systems to explain and justify their recommendations to network operators. We discuss forthcoming changes in network architecture which are likely to impact fault management and offer a vision of how fault management systems can exploit deep learning in the future. We identify a series of research topics for further study in order to achieve this