The development of several popular social networks in recent days and publication of social network data has led to the danger of disclosure of sensitive information of individuals. This necessitated the preservation of privacy before the publication of such data. There are several algorithms developed to preserve privacy in micro data. But these algorithms cannot be applied directly as in social networks the nodes have structural properties along with their labels. k-anonymity and l-diversity are efficient tools to anonymise micro data. So efforts have been made to find out similar algorithms to handle social network anonymisation. In this paper we propose an algorithm which can be used to achieve k-anonymity and l-diversity in social network anonymisation. This algorithm is based upon some existing algorithms developed in this direction. © 2012 IEEE.

Balakrushna Tripathy

Department of Analytics

School of Computer Science and Engineering

Vellore Campus

Mitra A.

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

2012 Fourth International Conference on Computational Aspects of Social Networks (CASoN)

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.

Fulltext

Asian Journal of Pharmaceutical and Clinical Research

A SURVEY ON PRIVACY PRESERVING TECHNIQUES FOR SOCIAL NETWORK DATA

Social Networking Intelligent Systems Reference Library

Privacy and Anonymization in Social Networks

The proliferation of social networks in digital media has proved to be fruitful, but this rise in popularity is accompanied by user privacy concerns. Social network data has been published in various ways and preserving the privacy of individuals in the published data has become an important concern. Several algorithms have been developed for privacy preservation in relational data, but these algorithms cannot be applied directly to social networks as the nodes here have structural properties along with labels. In this paper, we propose an algorithm to achieve κ-anonymity and l-diversity in social network data which provides structural anonymity along with sensitive attribute protection. The proposed algorithm uses novel edge addition techniques which are also presented in this paper. We also propose a concept of partial anonymity to reduce anonymization cost for d&gt;1. The empirical study shows that our algorithm requires significantly less number of edge additions for anonymization of social network data and has a substantially lower running time than the other algorithms previously proposed in the field. Copyright 2013 ACM.

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining - ASONAM '13

GASNA

Scientific study of network data can reveal many important behaviors of the elements involved and social trends. It also provides insight for suitable changes in the social structure and roles of individuals in it. There are many evidences (HIPAA (2002) Health insurance portability and accountability act. Available online http://www.hhs.gov/ocr/hipaa; Lambert, J Off Stat 9:313-331, 1993; Xu (2006) Utility based anonymisation using local recording. In: KDD'06, Philadelphia) which indicate the precious value of social network data in shedding light on social behavior, health, and well-being of the general public. For this purpose, the social network information needs to be published publicly or before a specialized group. But, depending upon the privacy model considered, this information may involve some sensitive data of individual participants in the social network, which are undesirable to be disclosed. Due to this problem, social network data need to be anonymized before its publication in order to prevent potential reidentification attacks. Data anonymization techniques are abundantly used in relational databases (Aggarwal et al. J Priv Technol, 2005; Backstrom et al. (2007) Wherefore art thou R3579X? Anonymized social networks, hidden patterns, and structural steganography. In: International world wide web conference (WWW). ACM, New York, pp 181-190; Bayardo and Agrawal (2005) Data privacy through optimal k-anonymisation. In: IEEE 21st international conference on data engineering, April 2005; Bamba et al. (2008) Supporting anonymous location queries in mobile environments with privacy grid. In: ACM world wide web conference; Byun et al. (2007) Efficient k-anonymisation using clustering techniques. In: International conference on database systems for advanced applications (DASFAA), pp 188-200; Campan and Truta (2008) A clustering approach for data and structural anonymity in social networks. In: ACM SIGKDD workshop on privacy, security, and trust in KDD (PinKDD), Las Vegas; Chakrabarti et al. (2004) R-MAT: a recursive model for graph mining. In: SIAM international conference on data mining; Chawla et al. (2005) Toward privacy in public databases. In: Proceedings of the theory of cryptography conference, Cambridge, MA; Evfimievski et al. (2003) Limiting privacy breaches in privacy preserving data mining. In: ACM principles of database systems (PODS). ACM, New York, pp 211-222; Getoor and Diehl, A surv SIGKDD Explore Newsl 7(2):3-12, 2005; Ghinita et al. (2007) Fast data anonymisation with low information loss. In: Very large data base conference (VLDB), Vienna, pp 758-769; Lefebvre et al. (2006) Mondrian multidimensional K-anonymity. In: IEEE international conference of data engineering (ICDE), p 25; Liu and Terzi (2008) Towards identity anonymisation on graphs. In: Wang (ed.) SIGMOD conference. ACM, New York, pp 93-106; Lunacek et al. (2006) A crossover operator for the k-anonymity problem. In: Genetic and evolutionary computation conference (GECCO), Seattle, Washington, pp 1713-1720; Machanavajjhala et al. (2006) L-diversity: privacy beyond K-anonymity. In: IEEE international conference on data engineering (ICDE), Atlanta, p 24; Malin, J Am Med Inform Assoc 12(1):28-34, 2004; Nergiz and Clifton (2006) Thoughts on k-anonymisation. In: IEEE 22nd international conference on data engineering workshops (ICDEW), Atlanta, April 2006, p 96; Nergiz and Clifton (2007) Multirelational k-anonymity. In: IEEE 23rd international conference on data engineering posters, April 2007). However, most of the known anonymisation approaches such as suppression or generalization do not directly apply to social network data. One major challenge in social network anonymization is the complexity. In (Gross and Yellen (2006) Graph theory and its applications. CRC, Boca Raton), it has been proved that a particular k-anonymity problem trying to minimize the structural change to the original social network is NP-hard. Research in anonymization of social networks is a relatively new field. In this chapter, we provide a systematic study of different approaches and studies done so far in this direction. There is no doubt that social network nodes can have imprecise data as their attributes. So, normal methods proposed for anonymization are not suitable for such type of social networks. Recently, a very efficient rough set-based algorithm was established in (Tripathy and Prakash Kumar, Int J Rapid Manuf 1(2):189-207, 2009) to handle clustering of tuples in relational models. We shall describe how this algorithm can be used for anonymization of social networks. Also, we shall present some recent algorithms which use isomorphism of graphs for anonymization of social networks. In the end, we shall discuss the current status of research on anonymization of social networks and present some related problems for further study. © 2012 Springer-Verlag London. All rights reserved.

Computational Social Networks

Anonymisation of Social Networks and Rough Set Approach

Use of published organizational data for a variety of purposes has the chance of violation of leakage of individual secret information. Though this is taken care by the organizations by removal or encryption of explicit identifiers, valuable information may still be leaked by quasi identifiers in the released data. The concept of k-anonymity was introduced and several algorithms in this direction have been proposed by different researchers [1, 2, 3, 4, 5, 6, 7, 8, 11] to handle this problem. But the notion of k-anonymity is susceptible to two types of attacks which necessitated the requirement of a better privacy preserving notion leading to the proposal of l- diversity [6]. In [14] a third phase is added to the two phase clustering-based k-anonymisation algorithm OKA [4] to achieve l-diversity. Recently, the clustering stage of the algorithm has been improved in [14] and the diversity stage algorithm is improved in [16] to come up with a fast l-diversity algorithm which deals with a single sensitive attribute in a relational table. Our main contribution in this paper is to develop an l-diversity algorithm to handle multi-sensitive attributes in databases. Also, we shall improve the adjustment stage algorithm so that it becomes more efficient. We also analyse and provide enough reasons to show that though the second and third stages of the algorithm are not necessary in most of the cases, we cannot avoid using these two stages in some cases. © 2011 IEEE.

2011 IEEE Recent Advances in Intelligent Computational Systems

A fast p-sensitive l-diversity Anonymisation algorithm

Now a days, more and more of social network data are being published in one way or other. So, preserving privacy in publishing social network data has become an important concern. With some local knowledge about individuals in a social network, an adversary may attack the privacy of some victims easily. Most of the work done so far towards privacy preservation can deal with relational data only. However, Bin Zhou and Jian Pei [11] proposed a scheme for anonymization of social networks, which is an initiative in this direction and provides a partial solution to this problem. In fact, their algorithm cannot handle the situations in which an adversary has knowledge about vertices in the second or higher hops of a vertex, in addition to its immediate neighbors. In this paper, we propose a modification to their algorithm for the network anonymization which can handle such situations. In doing so, we use an algorithm for graph isomorphism based on adjacency matrix instead of their approach using DFS technique [11]. More importantly, the time complexity of our algorithm is less than that of Zhou and Pei. © 2010 IEEE.

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