Providing efficient data privacy and data aggregation simultaneously is a challenging work in wireless sensor networks. In this paper, we propose a novel queue based privacy-preserving data aggregation scheme for additive aggregation function. In the scheme, sensor nodes are divided into clusters in a distributed way first, and then, in each cluster, cluster members will form a queue and adopt ...

The publication of a patient’s dataset is essential for various medical investigations and decision-making. Currently, significant focus has been established to protect privacy during data publishing. existing models multiple sensitive attributes do not concentrate on the correlation among attributes, which in turn leads much utility loss. An efficient model Heap Bucketization-anonymity (HBA) p...

It’s an open secret that search engines collect detailed query logs, and sometimes release these data to third parties. While making this wealth of information available provides enormous opportunities for information retrieval and web mining research, it also raises serious concerns about the privacy of individuals. We strongly believe that this data should be published to allow researchers to...

Personal data are the new oil. Vast amounts of spatiotemporal data generated by individuals have been collected and analyzed, such as check-in data, trajectories, web browsing data and timestamped medical records. These personal data are a valuable resource for businesses and also have the potential to provide significant social benefits through sharing and reuse. However, privacy concerns hind...

Privacy-preserving is one of the most important challenges in a computer world, because of the huge amount of sensitive information on the internet. The paper contains several privacy preservation techniques for data publishing in the real world. There are several privacy attacks are associate but among of them mainly two attacks are record linkage and attribute linkage. Many scientists have pr...

Releasing frequent sequential patterns can compromise individual privacy of underlying sequences. We propose two concrete objectives as a potential standard for privacy-preserving publishing sequential patterns: k-anonymity and α-dissociation. The first one, extended from k-anonymity model for data, addresses the problem of inferring patterns with very low support, say, in [1, k) where k is an ...

Moving Objects Databases (MOD) have gained popularity as a subject for research due to the latest developments in the positioning technologies and mobile networking. Analysis of mobility data can be used to discover and deliver knowledge that can enhance public welfare. For instance, a study of traffic patterns and congestion trends can reveal some information that can be used to improve routin...

Differential privacy is a strong notion for protecting individual privacy in privacy preserving data analysis or publishing. In this paper, we study the problem of differentially private histogram release based on an interactive differential privacy interface. We propose two multidimensional partitioning strategies including a baseline cell-based partitioning and an innovative kd-tree based par...