ﻻ يوجد ملخص باللغة العربية
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.
Privacy is an increasingly important aspect of data publishing. Reasoning about privacy, however, is fraught with pitfalls. One of the most significant is the auxiliary information (also called external knowledge, background knowledge, or side inform
A major feature of the emerging geo-social networks is the ability to notify a user when one of his friends (also called buddies) happens to be geographically in proximity with the user. This proximity service is usually offered by the network itself
Big Data is used by data miner for analysis purpose which may contain sensitive information. During the procedures it raises certain privacy challenges for researchers. The existing privacy preserving methods use different algorithms that results int
Artificial neural network has achieved unprecedented success in the medical domain. This success depends on the availability of massive and representative datasets. However, data collection is often prevented by privacy concerns and people want to ta
We give a fast algorithm to optimally compose privacy guarantees of differentially private (DP) algorithms to arbitrary accuracy. Our method is based on the notion of privacy loss random variables to quantify the privacy loss of DP algorithms. The ru