Privometer: Privacy protection in social networks

The increasing popularity of social networks, such as Facebook and Orkut, has raised several privacy concerns. Traditional ways of safeguarding privacy of personal information by hiding sensitive attributes are no longer adequate. Research shows that probabilistic classification techniques can effectively infer such private information. The disclosed sensitive information of friends, group affiliations and even participation in activities, such as tagging and commenting, are considered background knowledge in this process. In this paper, we present a privacy protection tool, called Privometer, that measures the amount of sensitive information leakage in a user profile and suggests selfsanitization actions to regulate the amount of leakage. In contrast to previous research, where inference techniques use publicly available profile information, we consider an augmented model where a potentially malicious application installed in the user’s friend profiles can access substantially more information. In our model, merely hiding the sensitive information is not sufficient to protect the user privacy. We present an implementation of Privometer in Facebook

Misusability Measure Based Sanitization of Big Data for Privacy Preserving MapReduce Programming

Leakage and misuse of sensitive data is a challenging problem to enterprises. It has become more serious problem with the advent of cloud and big data. The rationale behind this is the increase in outsourcing of data to public cloud and publishing data for wider visibility. Therefore Privacy Preserving Data Publishing (PPDP), Privacy Preserving Data Mining (PPDM) and Privacy Preserving Distributed Data Mining (PPDM) are crucial in the contemporary era. PPDP and PPDM can protect privacy at data and process levels respectively. Therefore, with big data privacy to data became indispensable due to the fact that data is stored and processed in semi-trusted environment. In this paper we proposed a comprehensive methodology for effective sanitization of data based on misusability measure for preserving privacy to get rid of data leakage and misuse. We followed a hybrid approach that caters to the needs of privacy preserving MapReduce programming. We proposed an algorithm known as Misusability Measure-Based Privacy serving Algorithm (MMPP) which considers level of misusability prior to choosing and application of appropriate sanitization on big data. Our empirical study with Amazon EC2 and EMR revealed that the proposed methodology is useful in realizing privacy preserving Map Reduce programming

Review on Present State-of-the-Art of Secure and Privacy Preserving Data Mining Techniques

As people of every walk of life are using Internet for various purposes there is growing evidence of proliferation of sensitive information. Security and privacy of data became an important concern. For this reason privacy preserving data mining (PPDM) has been an active research area. PPDM is a process discovering knowledge from voluminous data while protecting sensitive information. In this paper we explore the present state-of-the-art of secure and privacy preserving data mining algorithms or techniques which will help in real world usage of enterprise applications. The techniques discussed include randomized method, k-Anonymity, l-Diversity, t-Closeness, m-Privacy and other PPDM approaches. This paper also focuses on SQL injection attacks and prevention measures. The paper provides research insights into the areas of secure and privacy preserving data mining techniques or algorithms besides presenting gaps in the research that can be used to plan future research

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

Restrain On Social Networks From Conjecture Attacks

These Social networks allow their members to connect by means of various web linkes  in which the We study the problem of privacy-preservation in social networks. Now-a-days the use of social networks among the people has become more popular. With the impact of social networks on society, the people become more sensitive regarding privacy issues in the common networks. Anonymization of the social networks (MySpace, Facebook, Twitter and Orkut) is essential to preserve privacy of informations gathered by the social networks. Collection of techniques that use node attributes and the link structure to refine classifications.Uses local classifiers to establish a set of priors for each nodeUses traditional relational classifiers as the iterative step in classification

A privacy-preserving model to control social interaction behaviors in social network sites

Social Network Sites (SNSs) served as an invaluable platform to transfer information across a large number of users. SNSs also disseminate users data to third-parties to provide more interesting services for users as well as gaining profits. Users grant access to third-parties to use their services, although they do not necessarily protect users’ data privacy. Controlling social network data diffusion among users and third-parties is difficult due to the vast amount of data. Hence, undesirable users’ data diffusion to unauthorized parties in SNSs may endanger users’ privacy. This paper highlights the privacy breaches on SNSs and emphasizes the most significant privacy issues to users. The goals of this paper are to i) propose a privacy-preserving model for social interactions among users and third-parties; ii) enhance users’ privacy by providing access to the data for appropriate third-parties. These advocate to not compromising the advantages of SNSs information sharing functionalities

Location Cheating: A Security Challenge to Location-based Social Network Services

Location-based mobile social network services such as foursquare and Gowalla have grown exponentially over the past several years. These location-based services utilize the geographical position to enrich user experiences in a variety of contexts, including location-based searching and location-based mobile advertising. To attract more users, the location-based mobile social network services provide real-world rewards to the user, when a user checks in at a certain venue or location. This gives incentives for users to cheat on their locations. In this report, we investigate the threat of location cheating attacks, find the root cause of the vulnerability, and outline the possible defending mechanisms. We use foursquare as an example to introduce a novel location cheating attack, which can easily pass the current location verification mechanism (e.g., cheater code of foursquare). We also crawl the foursquare website. By analyzing the crawled data, we show that automated large scale cheating is possible. Through this work, we aim to call attention to location cheating in mobile social network services and provide insights into the defending mechanisms.Comment: 10 pages, 8 figures, accepted by the 31st International Conference on Distributed Computing Systems (ICDCS 2011

Facebook users have become much more private: A large-scale study

Abstract—We investigate whether Facebook users have become more private in recent years. Specifically, we examine if there have been any important trends in the information Facebook users reveal about themselves on their public profile pages since early 2010. To this end, we have crawled the public profile pages of 1.4 million New York City (NYC) Facebook users in March 2010 and again in June 2011. We have found that NYC users in our sample have become dramatically more private during this period. For example, in March 2010 only 17.2 % of users in our sample hid their friend lists, whereas in June 2011, just 15 months later, 52.6 % of the users hid their friend lists. We explore privacy trends for several personal attributes including friend list, networks, relationship, high school name and graduation year, gender, and hometown. We find that privacy trends have become more pronounced for certain demographics. Finally, we attempt to determine the primary causes behind the dramatic decrease in the amount of information Facebook users reveal about themselves to the general public. I