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

Sampling labelled profile data for identity resolution

Identity resolution capability for social networking profiles is important for a range of purposes, from open-source intelligence applications to forming semantic web connections. Yet replication of research in this area is hampered by the lack of access to ground-truth data linking the identities of profiles from different networks. Almost all data sources previously used by researchers are no longer available, and historic datasets are both of decreasing relevance to the modern social networking landscape and ethically troublesome regarding the preservation and publication of personal data. We present and evaluate a method which provides researchers in identity resolution with easy access to a realistically-challenging labelled dataset of online profiles, drawing on four of the currently largest and most influential online social networks. We validate the comparability of samples drawn through this method and discuss the implications of this mechanism for researchers as well as potential alternatives and extensions

Framework for Federated Learning Open Models in e-Government Applications

Using open data and artificial intelligence in providing innovative public services is the focus of the third generation of e-Government and supporting Internet and Communication Technologies systems. However, developing applications and offering open services based on (open) machine learning models requires large volumes of private, open, or a combination of both open and private data for model training to achieve sufficient model quality. Therefore, it would be beneficial to use both open and private data simultaneously to fully use the potential that machine learning could grant to the public and private sectors. Federated learning, as a machine learning technique, enables collaborative learning among different parties and their data, being private or open, creating shared knowledge by training models on such partitioned data without sharing it between parties in any step of the training or inference process. This paper provides a practical layout for developing and sharing machine learning models in a federative and open manner called Federated Learning Open Model. The definition of the Federated Learning Open Model concept is followed by a description of two potential use cases and services achieved with its usage, one being from the agricultural sector with the horizontal dataset partitioning and the latter being from the financial sector with a dataset partitioned vertically

How to Hide One's Relationships from Link Prediction Algorithms

Our private connections can be exposed by link prediction algorithms. To date, this threat has only been addressed from the perspective of a central authority, completely neglecting the possibility that members of the social network can themselves mitigate such threats. We fill this gap by studying how an individual can rewire her own network neighborhood to hide her sensitive relationships. We prove that the optimization problem faced by such an individual is NP-complete, meaning that any attempt to identify an optimal way to hide one’s relationships is futile. Based on this, we shift our attention towards developing effective, albeit not optimal, heuristics that are readily-applicable by users of existing social media platforms to conceal any connections they deem sensitive. Our empirical evaluation reveals that it is more beneficial to focus on “unfriending” carefully-chosen individuals rather than befriending new ones. In fact, by avoiding communication with just 5 individuals, it is possible for one to hide some of her relationships in a massive, real-life telecommunication network, consisting of 829,725 phone calls between 248,763 individuals. Our analysis also shows that link prediction algorithms are more susceptible to manipulation in smaller and denser networks. Evaluating the error vs. attack tolerance of link prediction algorithms reveals that rewiring connections randomly may end up exposing one’s sensitive relationships, highlighting the importance of the strategic aspect. In an age where personal relationships continue to leave digital traces, our results empower the general public to proactively protect their private relationships.M.W. was supported by the Polish National Science Centre grant 2015/17/N/ST6/03686. T.P.M. was supported by the Polish National Science Centre grants 2016/23/B/ST6/03599 and 2014/13/B/ST6/01807 (for this and the previous versions of this article, respectively). Y.V. and K.Z. were supported by ARO MURI (grant #W911NF1810208). Y.V. was also supported by the U.S. National Science Foundation (CAREER award IIS- 1905558 and grant IIS-1526860). E.M. acknowledges funding by Ministerio de Economa y Competitividad (Spain) through grant FIS2016-78904-C3-3-P

A Literature Survey and Classifications on Data Deanonymisation

The problem of disclosing private anonymous data has become increasingly serious particularly with the possibility of carrying out deanonymisation attacks on publishing data. The related work available in the literature is inadequate in terms of the number of techniques analysed, and is limited to certain contexts such as Online Social Networks. We survey a large number of state-of-the-art techniques of deanonymisation achieved in various methods and on different types of data. Our aim is to build a comprehensive understanding about the problem. For this survey, we propose a framework to guide a thorough analysis and classifications. We are interested in classifying deanonymisation approaches based on type and source of auxiliary information and on the structure of target datasets. Moreover, potential attacks, threats and some suggested assistive techniques are identified. This can inform the research in gaining an understanding of the deanonymisation problem and assist in the advancement of privacy protection

A Survey of Social Network Forensics

Social networks in any form, specifically online social networks (OSNs), are becoming a part of our everyday life in this new millennium especially with the advanced and simple communication technologies through easily accessible devices such as smartphones and tablets. The data generated through the use of these technologies need to be analyzed for forensic purposes when criminal and terrorist activities are involved. In order to deal with the forensic implications of social networks, current research on both digital forensics and social networks need to be incorporated and understood. This will help digital forensics investigators to predict, detect and even prevent any criminal activities in different forms. It will also help researchers to develop new models / techniques in the future. This paper provides literature review of the social network forensics methods, models, and techniques in order to provide an overview to the researchers for their future works as well as the law enforcement investigators for their investigations when crimes are committed in the cyber space. It also provides awareness and defense methods for OSN users in order to protect them against to social attacks

Influencable autonomy and predictable freedom in the IoE

This thesis investigates how individuals can develop, exercise, and maintain autonomy and freedom in the presence of information technology. It is particularly interested in how information technology can impose autonomy constraints. The first part identifies a problem with current autonomy discourse: There is no agreed upon object of reference when bemoaning loss of or risk to an individual’s autonomy. Here, thesis introduces a pragmatic conceptual framework to classify autonomy constraints. In essence, the proposed framework divides autonomy in three categories: intrinsic autonomy, relational autonomy and informational autonomy. The second part of the thesis investigates the role of information technology in enabling and facilitating autonomy constraints. The analysis identifies eleven characteristics of information technology, as it is embedded in society, so-called vectors of influence, that constitute risk to an individual’s autonomy in a substantial way. These vectors are assigned to three sets that correspond to the general sphere of the information transfer process to which they can be attributed to, namely domain-specific vectors, agent-specific vectors and information recipient-specific vectors. The third part of the thesis investigates selected ethical and legal implications of autonomy constraints imposed by information technology. It shows the utility of the theoretical frameworks introduced earlier in the thesis when conducting an ethical analysis of autonomy-constraining technology. It also traces the concept of autonomy in the European Data Lawsand investigates the impact of cultural embeddings of individuals on efforts to safeguard autonomy, showing intercultural flashpoints of autonomy differences. In view of this, the thesis approaches the exercise and constraint of autonomy in presence of information technology systems holistically. It contributes to establish a common understanding of (intuitive) terminology and concepts, connects this to current phenomena arising out of ever-increasing interconnectivity and computational power and helps operationalize the protection of autonomy through application of the proposed frameworks