Data publishing generate much attention over the protection of individual privacy. In this paper, we show that it is necessary to reduce the steps of generalization in order to minimize information loss in privacy preserving data publishing, but sometimes the anonymous table on basis of the method could still be attacked when an attacker can possibly determine the privacy requirement and anonym...

Previous works about privacy preserving serial data publishing on dynamic databases have relied on unrealistic assumptions of the nature of dynamic databases. In many applications, some sensitive values changes freely while others never change. For example, in medical applications, the disease attribute changes with time when patients recover from one disease and develop another disease. Howeve...

Stevens Institute of Technology Hoboken, NJ, USA {hwang,[email protected]} Abstract. We study the privacy threat by publishing data that contains full functional dependencies (FFDs). We show that the cross-attribute correlations by FFDs can bring potential vulnerability to privacy. Unfortunately, none of the existing anonymization principles can effectively prevent against the FFD-based priv...

Big data describes very large data sets that have more diverse and complicated structure like weblogs, social media, email, sensors, and photographs. These less structured data and distinctiveness characteristics from traditional databases typically associated with extra complications in storing, analyzing and applying further procedures or extracting results. Big data analytics is the process ...

Privacy preserving in data mining [1] is one of the major and increasingly interested area of research under data security. Privacy will be provided for data at different levels such as, while publishing the data, at the time of retrieving result by preserving sensitive data without disclosing it. It is not just sufficient to preserve sensitive data without disclosing it, but also need to manip...

Recent emerging mobile and wearable technologies make it easy to collect personal spatiotemporal data such as activity trajectories in daily life. Publishing real-time statistics over trajectory streams produced by crowds of people is expected to be valuable for both academia and business, answering questions such as “How many people are in Kyoto Station now?” However, analyzing these raw data ...

Recently big data has become a hot research topic. The rising amounts of big data also increase the chance of violate the privacy of individuals. Since big data need high computational power and large storage, distributed systems are used. As multiple parties are concerned in these systems, the risk of privacy violation is improved. There have been a number of privacy-preserving methods develop...