Visible to the public Differential-Privacy-Based Correlation Analysis in Railway Freight Service Applications

TitleDifferential-Privacy-Based Correlation Analysis in Railway Freight Service Applications
Publication TypeConference Paper
Year of Publication2017
AuthorsShi, Y., Piao, C., Zheng, L.
Conference Name2017 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)
Date Publishedoct
ISBN Number978-1-5386-2209-4
KeywordsAlgorithm design and analysis, apriori, big data privacy, data privacy, data sharing, Differential privacy, Distributed databases, Handheld computers, human factors, Knowledge discovery, Metrics, policy, privacy, privacy protection, pubcrawl, railway freight service, Resiliency, Scalability

With the development of modern logistics industry railway freight enterprises as the main traditional logistics enterprises, the service mode is facing many problems. In the era of big data, for railway freight enterprises, coordinated development and sharing of information resources have become the requirements of the times, while how to protect the privacy of citizens has become one of the focus issues of the public. To prevent the disclosure or abuse of the citizens' privacy information, the citizens' privacy needs to be preserved in the process of information opening and sharing. However, most of the existing privacy preserving models cannot to be used to resist attacks with continuously growing background knowledge. This paper presents the method of applying differential privacy to protect associated data, which can be shared in railway freight service association information. First, the original service data need to slice by optimal shard length, then differential method and apriori algorithm is used to add Laplace noise in the Candidate sets. Thus the citizen's privacy information can be protected even if the attacker gets strong background knowledge. Last, sharing associated data to railway information resource partners. The steps and usefulness of the discussed privacy preservation method is illustrated by an example.

Citation Keyshi_differential-privacy-based_2017