Review Paper-Social networking with protecting sensitive labels in data Anonymization

The use of social network sites goes on increasing such as facebook, twitter, linkedin, live journal social network and wiki vote network. By using this, users find that they can obtain more and more useful information such as the user performance, private growth, dispersal of disease etc. It is also important that users private information should not get disclose. Thus, Now a days it is important to protect users privacy and utilization of social network data are challenging. Most of developer developed privacy models such as K-anonymity for protecting node or vertex reidentification in structure information. Users privacy models get forced by other user, if a group of node largely share the same sensitive labels then other users easily find out one’s data ,so that structure anonymization method is not purely protected. There are some previous approaches such as edge editing or node clustering .Here structural information as well as sensitive labels of individuals get considered using K-degree l-deversityanonymity model. The new approach in anonymization methodology is adding noise nodes. By considering the least distortion to graph properties,the development of new algorithm using noise nodes into original graph. Most important it will provide an analysis of no.of noise nodes added and their impact on important graph property

Anonymizing cybersecurity data in critical infrastructures: the CIPSEC approach

Cybersecurity logs are permanently generated by network devices to describe security incidents. With modern computing technology, such logs can be exploited to counter threats in real time or before they gain a foothold. To improve these capabilities, logs are usually shared with external entities. However, since cybersecurity logs might contain sensitive data, serious privacy concerns arise, even more when critical infrastructures (CI), handling strategic data, are involved. We propose a tool to protect privacy by anonymizing sensitive data included in cybersecurity logs. We implement anonymization mechanisms grouped through the definition of a privacy policy. We adapt said approach to the context of the EU project CIPSEC that builds a unified security framework to orchestrate security products, thus offering better protection to a group of CIs. Since this framework collects and processes security-related data from multiple devices of CIs, our work is devoted to protecting privacy by integrating our anonymization approach.Peer ReviewedPostprint (published version

Privacy-Preserving Data Publishing in the Cloud: A Multi-level Utility Controlled Approach

Conventional private data publication schemes are targeted at publication of sensitive datasets with the objective of retaining as much utility as possible for statistical (aggregate) queries while ensuring the privacy of individuals' information. However, such an approach to data publishing is no longer applicable in shared multi-tenant cloud scenarios where users often have different levels of access to the same data. In this paper, we present a privacy-preserving data publishing framework for publishing large datasets with the goals of providing different levels of utility to the users based on their access privileges. We design and implement our proposed multi-level utility-controlled data anonymization schemes in the context of large association graphs considering three levels of user utility namely: (i) users having access to only the graph structure (ii) users having access to graph structure and aggregate query results and (iii) users having access to graph structure, aggregate query results as well as individual associations. Our experiments on real large association graphs show that the proposed techniques are effective, scalable and yield the required level of privacy and utility for user-specific utility and access privilege levels

A SURVEY ON PRIVACY PRESERVING TECHNIQUES FOR SOCIAL NETWORK DATA

In this era of 20th century, online social network like Facebook, twitter, etc. plays a very important role in everyone's life. Social network data, regarding any individual organization can be published online at any time, in which there is a risk of information leakage of anyone's personal data. So preserving the privacy of individual organizations and companies are needed before data is published online. Therefore the research was carried out in this area for many years and it is still going on. There have been various existing techniques that provide the solutions for preserving privacy to tabular data called as relational data and also social network data represented in graphs. Different techniques exists for tabular data but you can't apply directly to the structured complex graph  data,which consists of vertices represented as individuals and edges representing some kind of connection or relationship between the nodes. Various techniques like K-anonymity, L-diversity, and T-closeness exist to provide privacy to nodes and techniques like edge perturbation, edge randomization are there to provide privacy to edges in social graphs. Development of new techniques by  Integration to exiting techniques like K-anonymity ,edge perturbation, edge randomization, L-Diversity are still going on to provide more privacy to relational data and social network data are ongoingin the best possible manner.Â