Estudi de la privacitat de dades

A causa de la imminent digitalització de la informació personal que s'acumula en els fitxers dels estats, empreses i xarxes socials, la preservació de la privacitat està sent un dels trencaclosques més difícils d'afrontar i mitigar en aquests últims anys, tant pels responsables de la seguretat de les dades així com pels mateixos usuaris d'Internet. En aquest paper es presenta l'estat de l'art actual d'alguns dels algoritmes que busquen protegir la privacitat de les dades, analitzant les possibles amenaces i riscos implicats. Així com una descripció de les dues línies de treball més estudiades actualment, com són el Graph-Modification i el Differential Privacy, on es realitzarà una explicació de cadascuna de les tècniques emprades, fent especial referencia a la modificació en les arestes i els vèrtexs, random perturbation i k-anonimitat. Finalment i emprant un dataset d'una xarxa d'interconnexions com un graf, es mostrarà la comparativa d'un conjunt d'indicadors que avaluen la pèrdua d'informació que es produeix a l'hora d'anonimitzar les dades segons l'algoritme d'anonimització escollit, a partir d'un conjunt de proves empíriques realitzades sobre el dataset original.Due to the impending digitalization of personal information stored in states and companies files and social networks, the preservation of privacy is being one of the most difficult puzzles to mitigate and cope with in recent years, by those who are responsible for the security of such data security as well as Internet users themselves. This paper presents the current state of art of some of the algorithms which target is to protect the privacy of the data, by analyzing the potential threats and risks involved. Also a description of the two lines of work studied nowadays, such as Graph-Modification and Differential Privacy, where there will be an explanation of each of the techniques used, with a particular reference to the modifications in the edges and vertices, random perturbation and k-anonymity. Finally, from a set of empirical tests performed on the dataset of a network of interconnections used as a graph, will be shown the comparison of a set of indicators that evaluate the information loss produced as a result of the anonymization process chosen.Debido a la inminente digitalización de la información personal que se acumula en los ficheros de los estados, empresas y redes sociales, la preservación de la privacidad está siendo uno de los rompecabezas más difíciles de afrontar y mitigar en estos últimos años, ya sea tanto por los responsables de la seguridad de los datos así como por los usuarios de Internet. En este papel se presenta el estado del arte actual de algunos de los algoritmos que tienen como objetivo proteger la privacidad de los datos, analizando las posibles amenazas y riesgos implicados. Así como una descripción de las dos principales líneas de trabajo que hay actualmente, como son Graph-Modification y Differential Privacy, donde se realizará una explicación de cada una de las técnicas empleadas, haciendo especial referencia a la modificación en las aristas y los vértices, random perturbation y k-anonimitat. Finalmente y empleando un dataset de una red de interconexiones como un grafo, se mostrará la comparativa de un conjunto de indicadores que evalúan la pérdida de información que se produce a la hora de anonimitzar los datos según el algoritmo de anonimización escogido, a partir de un conjunto de pruebas empíricas realizadas sobre el dataset original

Graph Perturbation as Noise Graph Addition: A New Perspective for Graph Anonymization

Different types of data privacy techniques have been applied to graphs and social networks. They have been used under different assumptions on intruders’ knowledge. i.e., different assumptions on what can lead to disclosure. The analysis of different methods is also led by how data protection techniques influence the analysis of the data. i.e., information loss or data utility. One of the techniques proposed for graph is graph perturbation. Several algorithms have been proposed for this purpose. They proceed adding or removing edges, although some also consider adding and removing nodes. In this paper we propose the study of these graph perturbation techniques from a different perspective. Following the model of standard database perturbation as noise addition, we propose to study graph perturbation as noise graph addition. We think that changing the perspective of graph sanitization in this direction will permit to study the properties of perturbed graphs in a more systematic way

Private Graph Data Release: A Survey

The application of graph analytics to various domains have yielded tremendous societal and economical benefits in recent years. However, the increasingly widespread adoption of graph analytics comes with a commensurate increase in the need to protect private information in graph databases, especially in light of the many privacy breaches in real-world graph data that was supposed to preserve sensitive information. This paper provides a comprehensive survey of private graph data release algorithms that seek to achieve the fine balance between privacy and utility, with a specific focus on provably private mechanisms. Many of these mechanisms fall under natural extensions of the Differential Privacy framework to graph data, but we also investigate more general privacy formulations like Pufferfish Privacy that can deal with the limitations of Differential Privacy. A wide-ranging survey of the applications of private graph data release mechanisms to social networks, finance, supply chain, health and energy is also provided. This survey paper and the taxonomy it provides should benefit practitioners and researchers alike in the increasingly important area of private graph data release and analysis

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

Privacy, Access Control, and Integrity for Large Graph Databases

Graph data are extensively utilized in social networks, collaboration networks, geo-social networks, and communication networks. Their growing usage in cyberspaces poses daunting security and privacy challenges. Data publication requires privacy-protection mechanisms to guard against information breaches. In addition, access control mechanisms can be used to allow controlled sharing of data. Provision of privacy-protection, access control, and data integrity for graph data require a holistic approach for data management and secure query processing. This thesis presents such an approach. In particular, the thesis addresses two notable challenges for graph databases, which are: i) how to ensure users\u27 privacy in published graph data under an access control policy enforcement, and ii) how to verify the integrity and query results of graph datasets. To address the first challenge, a privacy-protection framework under role-based access control (RBAC) policy constraints is proposed. The design of such a framework poses a trade-off problem, which is proved to be NP-complete. Novel heuristic solutions are provided to solve the constraint problem. To the best of our knowledge, this is the first scheme that studies the trade-off between RBAC policy constraints and privacy-protection for graph data. To address the second challenge, a cryptographic security model based on Hash Message Authentic Codes (HMACs) is proposed. The model ensures integrity and completeness verification of data and query results under both two-party and third-party data distribution environments. Unique solutions based on HMACs for integrity verification of graph data are developed and detailed security analysis is provided for the proposed schemes. Extensive experimental evaluations are conducted to illustrate the performance of proposed algorithms

A survey of graph-modification techniques for privacy-preserving on networks

Recently, a huge amount of social networks have been made publicly available. In parallel, several definitions and methods have been proposed to protect users' privacy when publicly releasing these data. Some of them were picked out from relational dataset anonymization techniques, which are riper than network anonymization techniques. In this paper we summarize privacy-preserving techniques, focusing on graph-modification methods which alter graph's structure and release the entire anonymous network. These methods allow researchers and third-parties to apply all graph-mining processes on anonymous data, from local to global knowledge extraction

A survey of graph-modification techniques for privacy-preserving on networks

Recently, a huge amount of social networks have been made publicly available. In parallel, several definitions and methods have been proposed to protect users' privacy when publicly releasing these data. Some of them were picked out from relational dataset anonymization techniques, which are riper than network anonymization techniques. In this paper we summarize privacy-preserving techniques, focusing on graph-modification methods which alter graph's structure and release the entire anonymous network. These methods allow researchers and third-parties to apply all graph-mining processes on anonymous data, from local to global knowledge extraction