ARCHITECTURAL AND ENGINEERING DESIGN FOR FINDING LOCATION ON CROWDED PUBLIC AREAS

Abstract

In the research of location privacy protection, the existing methods are mostly based on the traditional
anonymization, fuzzy and cryptography technology, and there was a little success in the big data environment,
for example, the sensor network consists of sensitive information, that is compulsory to be protected
compulsorily . Current trends such as "Industrie 4.0" and the Internet of Things (IoT), which generate,
processes , and exchange large amounts of security-critical and privacy-sensitive data, which makes them
attractive targets of attacks. However, previous methods overlooked the privacy protection issue, which lead
them to privacy violation. In this paper, we propose a location privacy protection method which satisfies
differential privacy constraint to protect location data privacy and maximize the data utility and algorithm utility
in Industrial Internet of Things. In view of the high value and low density of location data, we combine both
utility and privacy together and build a multilevel location information tree model.Furthermore, the differential
privacy index mechanism is used to select the data according to the accessing frequency of the tree node
Finally, the Laplace scheme is used for the particular data to add noises in the accessing frequency . As shown
in the theoretical analysis and the experimental results, the proposed strategy can achieve vast improvements in
terms of applicability ,security and privacy.