A Comprehensive Bibliometric Analysis on Social Network Anonymization: Current Approaches and Future Directions

In recent decades, social network anonymization has become a crucial research field due to its pivotal role in preserving users' privacy. However, the high diversity of approaches introduced in relevant studies poses a challenge to gaining a profound understanding of the field. In response to this, the current study presents an exhaustive and well-structured bibliometric analysis of the social network anonymization field. To begin our research, related studies from the period of 2007-2022 were collected from the Scopus Database then pre-processed. Following this, the VOSviewer was used to visualize the network of authors' keywords. Subsequently, extensive statistical and network analyses were performed to identify the most prominent keywords and trending topics. Additionally, the application of co-word analysis through SciMAT and the Alluvial diagram allowed us to explore the themes of social network anonymization and scrutinize their evolution over time. These analyses culminated in an innovative taxonomy of the existing approaches and anticipation of potential trends in this domain. To the best of our knowledge, this is the first bibliometric analysis in the social network anonymization field, which offers a deeper understanding of the current state and an insightful roadmap for future research in this domain.Comment: 73 pages, 28 figure

Markov Models for Network-Behavior Modeling and Anonymization

Modern network security research has demonstrated a clear need for open sharing of traffic datasets between organizations, a need that has so far been superseded by the challenge of removing sensitive content beforehand. Network Data Anonymization (NDA) is emerging as a field dedicated to this problem, with its main direction focusing on removal of identifiable artifacts that might pierce privacy, such as usernames and IP addresses. However, recent research has demonstrated that more subtle statistical artifacts, also present, may yield fingerprints that are just as differentiable as the former. This result highlights certain shortcomings in current anonymization frameworks -- particularly, ignoring the behavioral idiosyncrasies of network protocols, applications, and users. Recent anonymization results have shown that the extent to which utility and privacy can be obtained is mainly a function of the information in the data that one is aware and not aware of. This paper leverages the predictability of network behavior in our favor to augment existing frameworks through a new machine-learning-driven anonymization technique. Our approach uses the substitution of individual identities with group identities where members are divided based on behavioral similarities, essentially providing anonymity-by-crowds in a statistical mix-net. We derive time-series models for network traffic behavior which quantifiably models the discriminative features of network "behavior" and introduce a kernel-based framework for anonymity which fits together naturally with network-data modeling

Achieving Location Privacy in iOS Platform Using Location Privacy Framework

Rising popularity of location-services mobile applications and geotagging digitalactivities resulted in astonishing amount of mobility data collected from user devices, raising privacy concerns regarding the way this data is extracted and handled. Despite numerous studies concluded that human location trace is highly unique and poses great re-identification risks, modern mobile operating systems fell short of implementing granular location access mechanism. Existing binary location access resulted into location-based-services being able to retrieve precise user’s coordinates regardless of how much details their functionality actually require and sell it to data brokers. This paper aims to provide practical solution how a mobile operating system (iOS) can adopt a system that enforces better location privacy for user devices with Location Privacy Framework(LPF) that works as a trusted middleware between mobile operating system and third-party apps. LPF provides granulated way of extracting location-related data from device, maximizing privacy by applying geomasking algorithm based on minimum level of accuracy the app needs and ensuring k-anonymity with dummy-generation mechanisms. Furthermore, LPF enforces control over all location data network communication to and from the app to make sure that no identifying data is being shared with data brokers

A Privacy by Design Methodology Application in Telecom Domain

Telecommunication has been considerably developed over the last decades, notably through optical fiber submarine cables and wireless connections offering voice and data wide range services. Telecommunication infrastructures are the necessary backbone that make possible any voice and data exchange. Unfortunately, these infrastructures are still suffering from various vulnerabilities and continue to be target of specific cyber-attacks. Some of these attacks could lead to service deniability, integrity and privacy loss. Against this fact, it’s also established that telecom service providers, as the owner of this infrastructure,can have access to huge data,                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            even personal data related to customer and  to their employees. Basically, this personal data is related directly to the customer’s and employee’s identity, geolocation, interest areas and contact circle, etc.,  when it comes to the use of this personal data, the privacy concerns become a big challenge for telecom service providers due to heavy impact that can induce. Given the personal data protection criticality in telecom domain, privacy by design PbD should be incorporate. Then, this article aims to apply in telecom service providers ISPM methodology "Information System Privacy Methodology" which focuses on PbD implementation in enterprises architecture, specifically in information systems taking into account all technical and organizational aspects