The continuous advances in sensing and positioning technologies have resulted in a dramatic increase in popularity of LocationBased Services (LBS). Nevertheless, the LBS can lead to user privacy breach due to sharing location information with potentially malicious services. A high degree of location privacy preservation for LBS is extremely required. In this paper, a clustering K-anonymity sche...

Recently, privacy preserving data publishing has attracted significant interest in research. Most of the existing studies focus on only the situations where the data in question is published using one quasi-identifier. However, in a few important applications, a practical demand is to publish a data set on multiple quasi-identifiers for multiple users simultaneously, which poses several challen...

Aside from smart technologies, farm data collection is also important for farms including environment and farmer survey collection. With collection, we observe that it generally proposed to utilize in systems. However, can be released use the outside scope of collecting organization an appropriate business reason such as improving system, product quality, customer service. Moreover, often inclu...

Ling Guo. Randomization Based Privacy Preserving Categorical Data Analysis. Under the direction of Dr. Xintao Wu The success of data mining relies on the availability of high quality data. To ensure quality data mining, effective information sharing between organizations becomes a vital requirement in today’s society. Since data mining often involves sensitive information of individuals, the pu...

Time-stamped location information is regarded as spatio-temporal data due to its time and space dimensions and, by its nature, is highly vulnerable to misuse. Privacy issues related to collection, use and distribution of individuals’ location information are the main obstacles impeding knowledge discovery in spatio-temporal data. Suppressing identifiers from the data does not suffice since move...

Privacy preservation is a crucial issue for smart buildings where all kinds of messages, e.g., power usage data, control commands, events, alarms, etc. are transmitted to accomplish the management of power. Without appropriate privacy protection schemes, electricity customers are faced with various privacy risks. Meanwhile, the natures of smart grids and smart buildings—such as having limited c...

A large number of cloud services require users to share private data like electronic health records for data analysis or mining, bringing privacy concerns. Anonymizing data sets via generalization to satisfy certain privacy requirements such as k-anonymity is a widely used category of privacy preserving techniques. At present, the scale of data in many cloud applications increases tremendously ...

Privacy preservation is a main motivation for using the DCOP model and as such, it has been the subject of comprehensive research. The present paper provides for the first time a categorization of all possible DCOP privacy types. The paper focuses on a specific type, internal constraint privacy, which is highly relevant for models that enable asymmetric payoffs (PEAV-DCOP and ADCOP). An analysi...

Data stream can be conceived as a continuous and changing sequence of data that continuously arrive at a system to store or process. Examples of data streams include computer network traffic, phone conversations, web searches and sensor data etc. The data owners or publishers may not be willing to exactly reveal the true values of their data due to various reasons, most notably privacy consider...

Protecting privacy in sensor networks poses new challenges because of the potential incompatibilities between new privacy-preserving mechanisms and mechanisms already implemented in sensor networks (such as in-network data aggregation). To address this problem, we propose in this paper a set of new privacy-preservation data aggregation schemes. Different from past research, our solutions have t...