PRIVAS - automatic anonymization of databases

Currently, given the technological evolution, data and information are increasingly valuable in the most diverse areas for the most various purposes. Although the information and knowledge discovered by the exploration and use of data can be very valuable in many applications, people have been increasingly concerned about the other side, that is, the privacy threats that these processes bring. The system Privas, described in this paper, will aid the Data Publisher to pre-process the database before publishing. For that, a DSL is used to define the database schema description, identify the sensitive data and the desired privacy level. After that a Privas processor will process the DSL program and interpret it to automatically transform the repository schema. The automatization of the anonymization process is the main contribution and novelty of this work.info:eu-repo/semantics/publishedVersio

Assuring data privacy with PRIVAS - a tool for data publishers

The technology of nowadays allows to easily extract, store, process and use information about individuals and organizations. The increase of the amount of data collected and its value to our society was, at first, a great advance that could be used to optimize processes, find solutions and support decisions but also brought new problems related with lack of privacy and malicious attacks to confidential information. In this paper, a tool to anonymize databases is presented. It can be used by data publishers to protect information from attacks controlling the desired privacy level and the data usefulness. In order to specify these requirements a DSL (PrivasL) is used and the automatization of repository transformation, that is based on language processing techniques, is the novelty of this work.FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019.info:eu-repo/semantics/publishedVersio

Protecting privacy of semantic trajectory

The growing ubiquity of GPS-enabled devices in everyday life has made large-scale collection of trajectories feasible, providing ever-growing opportunities for human movement analysis. However, publishing this vulnerable data is accompanied by increasing concerns about individuals’ geoprivacy. This thesis has two objectives: (1) propose a privacy protection framework for semantic trajectories and (2) develop a Python toolbox in ArcGIS Pro environment for non-expert users to enable them to anonymize trajectory data. The former aims to prevent users’ re-identification when knowing the important locations or any random spatiotemporal points of users by swapping their important locations to new locations with the same semantics and unlinking the users from their trajectories. This is accomplished by converting GPS points into sequences of visited meaningful locations and moves and integrating several anonymization techniques. The second component of this thesis implements privacy protection in a way that even users without deep knowledge of anonymization and coding skills can anonymize their data by offering an all-in-one toolbox. By proposing and implementing this framework and toolbox, we hope that trajectory privacy is better protected in research

A Clustering-Anonymity Approach for Trajectory Data Publishing Considering both Distance and Direction

Trajectory data contains rich spatio-temporal information of moving objects. Directly publishing it for mining and analysis will result in severe privacy disclosure problems. Most existing clustering-anonymity methods cluster trajectories according to either distance- or direction-based similarities, leading to a high information loss. To bridge this gap, in this paper, we present a clustering-anonymity approach considering both these two types of similarities. As trajectories may not be synchronized, we first design a trajectory synchronization algorithm to synchronize them. Then, two similarity metrics between trajectories are quantitatively defined, followed by a comprehensive one. Furthermore, a clustering-anonymity algorithm for trajectory data publishing with privacy-preserving is proposed. It groups trajectories into clusters according to the comprehensive similarity metric. These clusters are finally anonymized. Experimental results show that our algorithm is effective in preserving privacy with low information loss

Privacy, Space and Time: a Survey on Privacy-Preserving Continuous Data Publishing

Sensors, portable devices, and location-based services, generate massive amounts of geo-tagged, and/or location- and user-related data on a daily basis. The manipulation of such data is useful in numerous application domains, e.g., healthcare, intelligent buildings, and traffic monitoring, to name a few. A high percentage of these data carry information of users\u27 activities and other personal details, and thus their manipulation and sharing arise concerns about the privacy of the individuals involved. To enable the secure—from the users\u27 privacy perspective—data sharing, researchers have already proposed various seminal techniques for the protection of users\u27 privacy. However, the continuous fashion in which data are generated nowadays, and the high availability of external sources of information, pose more threats and add extra challenges to the problem. In this survey, we visit the works done on data privacy for continuous data publishing, and report on the proposed solutions, with a special focus on solutions concerning location or geo-referenced data

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

Location-Based Services and Privacy Protection under Mobile Cloud Computing

Location-based services can provide personalized services based on location information of moving objects and have already been widely used in public safety services, transportation, entertainment and many other areas. With the rapid development of mobile communication technology and popularization of intelligent terminals, there will be great commercial prospects to provide location-based services under mobile cloud computing environment. However, the high adhesion degree of mobile terminals to users not only brings facility but also results in the risk of privacy leak. The paper introduced the necessities and advantages to provide location-based services under mobile cloud computing environment, stressed the importance to protect location privacy in LBS services, pointed out new security risks brought by mobile cloud computing, and proposed a new framework and implementation method of LBS service. The cloud-based LBS system proposed in this paper is able to achieve privacy protection from the confidentiality of outsourced data and integrity of service results, and can be used as a reference while developing LBS system under mobile cloud computing environment