Abstract k -Anonymity is one of the most well-known privacy models. Internal and external attacks were discussed for this model, both focusing on categorical data. These can be seen as attribute disclosure a particular attribute. Then, p -sensitivity -diversity proposed solutions these That is, way to avoid very In paper we discuss case numerical data, show that also take place. For this, use r...

This paper presents a K-means clustering technique that satisfies the biobjective function to minimize the information loss and maintain k-anonymity. The proposed technique starts with one cluster and subsequently partitions the dataset into two or more clusters such that the total information loss across all clusters is the least, while satisfying the k-anonymity requirement. The structure of ...

Privacy-maintaining data release is one of the most important challenges in an information system, because of the wide collection of sensitive information on the internet. A number of solutions have been designed for privacy-maintaining data release. This paper provides an inspection of the state-of-theart methods for privacy protection. The paper discusses novel and powerful privacy definition...

Spatial-temporal k-anonymity has become a mainstream approach among techniques for protection of users' privacy in location-based services (LBS) applications, and has been applied to several variants such as LBS snapshot queries and continuous queries. Analyzing large-scale spatial-temporal anonymity sets may benefit several LBS applications. In this paper, we propose two location prediction me...

Modern software often provides automated testing and bug reporting facilities that enable developers to improve the software after release. Alas, this comes at the cost of user anonymity: reported execution traces may identify users. We present a way to mitigate this inherent tension between developer utility and user anonymity: automatically transform execution traces in a way that preserves t...

To release micro-data tables containing sensitive data, generalization algorithms are usually required for satisfying given privacy properties, such as k-anonymity and l-diversity. It is well accepted that k-anonymity and l-diversity are proposed for different purposes, and the latter is a stronger property than the former. However, this paper uncovers an interesting relationship between these ...

We present a novel hybrid communication protocol that guarantees mobile users’ k-anonymity against a wide-range of adversaries by exploiting the capability of handheld devices to connect to both WiFi and cellular networks. Unlike existing anonymity schemes, we consider all parties that can intercept communications between the mobile user and a server as potential privacy threats. We formally qu...

This paper examines major privacy concerns in location-based services. Most user privacy techniques are based on cloaking, which achieves location k-anonymity. The key is to reduce location resolution by ensuring that each cloaking area reported to a service provider contains at least k mobile users. However, maintaining location k-anonymity alone is inadequate when the majority of the k mobile...

Now a day’s governmental and nongovernmental organization wants to share their information for the purpose of knowledge discovery. When the data’s are shared individuals personal data or sensitive data which should not be known to others. Eg. Medical record, voters list, census data. This may leads a latest research field called Privacy preserving data mining. To address these issues, released ...