Engendering Unprecedented Activation of Oxygen Evolution via Rational Pinning of Ni Oxidation State in Prototypical Perovskite:Close Juxtaposition of Synthetic Approach and Theoretical Conception

Rational optimization of the OER activity of catalysts based on LaNiO3 oxide is achieved by maximizing the presence of trivalent Ni in the surface structure. DFT investigations of the LaNiO3 catalyst and surface structures related to it predict an improvement in the OER activity for these materials to levels comparable with the top of the OER volcano if the La content is minimized while the oxidation state of Ni is maintained. These theoretically predicted structures of high intrinsic OER activity can be prepared by a templated spray-freeze freeze-drying synthesis followed by a simple postsynthesis exfoliation-like treatment in acidic media. These nanocrystalline LaNiO3-related materials confirm the theoretical predictions, showing a dramatic improvement in OER activity. The exfoliated surfaces remain stable in OER catalysis, as shown by an in-operando ICP-OES study. The unprecedented OER activation of the synthesized LaNiO3-based materials is related to a close juxtaposition of the theoretical conception of ideal structural motifs and the ability to engender such motifs using a unique synthetic procedure, both principally related to stabilization and pinning of the Ni oxidation state within the local coordination environment of the perovskite structure. © 2021 American Chemical Society. All rights reserved

Utility Promises of Self-Organising Maps in Privacy Preserving Data Mining

Data mining techniques are highly efficient in sifting through big data to extract hidden knowledge and assist evidence-based decisions. However, it poses severe threats to individuals’ privacy because it can be exploited to allow inferences to be made on sensitive data. Researchers have proposed several privacy-preserving data mining techniques to address this challenge. One unique method is by extending anonymisation privacy models in data mining processes to enhance privacy and utility. Several published works in this area have utilised clustering techniques to enforce anonymisation models on private data, which work by grouping the data into clusters using a quality measure and then generalise the data in each group separately to achieve an anonymisation threshold. Although they are highly efficient and practical, however guaranteeing adequate balance between data utility and privacy protection remains a challenge. In addition to this, existing approaches do not work well with high-dimensional data, since it is difficult to develop good groupings without incurring excessive information loss. Our work aims to overcome these challenges by proposing a hybrid approach, combining self organising maps with conventional privacy based clustering algorithms. The main contribution of this paper is to show that, dimensionality reduction techniques can improve the anonymisation process by incurring less information loss, thus producing a more desirable balance between privacy and utility properties

Privacy enhancing technologies (PETs) for connected vehicles in smart cities

This is an accepted manuscript of an article published by Wiley in Transactions on Emerging Telecommunications Technologies, available online: https://doi.org/10.1002/ett.4173 The accepted version of the publication may differ from the final published version.Many Experts believe that the Internet of Things (IoT) is a new revolution in technology that has brought many benefits for our organizations, businesses, and industries. However, information security and privacy protection are important challenges particularly for smart vehicles in smart cities that have attracted the attention of experts in this domain. Privacy Enhancing Technologies (PETs) endeavor to mitigate the risk of privacy invasions, but the literature lacks a thorough review of the approaches and techniques that support individuals' privacy in the connection between smart vehicles and smart cities. This gap has stimulated us to conduct this research with the main goal of reviewing recent privacy-enhancing technologies, approaches, taxonomy, challenges, and solutions on the application of PETs for smart vehicles in smart cities. The significant aspect of this study originates from the inclusion of data-oriented and process-oriented privacy protection. This research also identifies limitations of existing PETs, complementary technologies, and potential research directions.Published onlin

Privacy in trajectory data

In this era of significant advances in telecommunications and GPS sensors technology, a person can be tracked down to proximity of less than 5 meters. This remarkable progress enabled the offering of services that depend on user location (the so-called location-based services-LBSs), as well as the existence of applications that analyze movement data for various purposes. However, without strict safeguards, both the deployment of LBSs and the mining of movement data come at a cost of privacy for the users, whose movement is recorded. This chapter studies privacy in both online and offline movement data. After introducing the reader to this field of study, we review state-of-the-art work for location and trajectory privacy both in LBSs and in trajectory databases. Then, we present a qualitative evaluation of these works, pointing out their strengths and weaknesses. We conclude the chapter by providing our point of view regarding the future trends in trajectory data privacy. © 2009, IGI Global

An integer programming approach for frequent itemset hiding

The rapid growth of transactional data brought, soon enough, into attention the need of its further exploitation. In this paper, we investigate the problem of securing sensitive knowledge from being exposed in patterns extracted during association rule mining. Instead of hiding the produced rules directly, we decide to hide the sensitive frequent itemsets that may lead to the production of these rules. As a first step, we introduce the notion of distance between two databases and a measure for quantifying it. By trying to minimize the distance between the original database and its sanitized version (that can safely be released), we propose a novel, exact algorithm for association rule hiding and evaluate it on real world datasets demonstrating its effectiveness towards solving the problem. Copyright 2006 ACM

Concealing the position of individuals in location-based services

The offering of location based services requires an in- depth knowledge of the subscriber's whereabouts. Thus, without the existence of strict safeguards, the deployment of such services may easily breach user privacy. To address this issue, special algorithms are necessary that anonymize user location information prior to its release to the service provider of the telecom operator. In this paper, we extend existing work in historical K- anonymity (1) by considering an underlying network of user movement and (2) by pushing the core functionality of the anonymizer into a spatiotemporal DBMS. The proposed scheme allows each individual to specify his/her anonymity requirements, involving a series of spatiotemporal regions that are considered as unsafe with respect to his/her privacy. When the user requests an LBS from within one of his unsafe regions, the anonymizer performs a spatial along with a temporal generalization of his request in order to protect the user's privacy. If the generalization algorithm fails to provide the necessary anonymity, the system dynamically constructs a mix- zone around the requester with the aim of unlinking his future requests from the previous ones. As the experimental results indicate, by utilizing the spatiotemporal capabilities of the used DBMS, the performance of the anonymizer improves when compared to existing work in historical K- anonymity

Exact Knowledge Hiding through Database Extension

In this paper, we propose a novel, exact border-based approach that provides an optimal solution for the hiding of sensitive frequent item sets by 1) minimally extending the original database by a synthetically generated database part-the database extension, 2) formulating the creation of the database extension as a constraint satisfaction problem, 3) mapping the constraint satisfaction problem to an equivalent binary integer programming problem, 4) exploiting underutilized synthetic transactions to proportionally increase the support of nonsensitive item sets, 5) minimally relaxing the constraint satisfaction problem to provide an approximate solution close to the optimal one when an ideal solution does not exist, and 6) using a partitioning in the universe of the items to increase the efficiency of the proposed hiding algorithm. Extending the original database for sensitive item set hiding is proved to provide optimal solutions to an extended set of hiding problems compared to previous approaches and to provide solutions of higher quality. Moreover, the application of binary integer programming enables the simultaneous hiding of the sensitive item sets and thus allows for the identification of globally optimal solutions

A hybrid approach to frequent itemset hiding

In this paper, we propose a novel, exact border-based approach that provides an optimal solution for the hiding of sensitive frequent itemsets by (i) minimally extending the original database by a synthetically generated database part - the database extension, (ii) formulating the creation of the database extension as a constraint satisfaction problem that is solved by using binary integer programming, and (iii) providing an approximate solution close to the optimal one when an ideal solution does not exist. Extending the original database for sensitive itemset hiding is proved to provide optimal solutions to an extended set of hiding problems compared to previous approaches and to provide solutions of higher quality. © 2007 IEEE

Hestia: Historically-enabled spatio-temporal information anonymity

Location-Based Services (LBSs) have long been established in several regions of the world to allow mobile users, equipped with positioning devices, access a set of spatially aware services. In this chapter, we introduce a privacy framework for LBSs that utilizes collected movement data to identify parts of the user trajectories, where user privacy is at an elevated risk. To protect the privacy of the user, the proposed methodology transforms the original requests into anonymous counterparts by offering trajectory K-anonymity. As a proof of concept, we build a working prototype that implements our solution approach and is mainly used for experimentation and evaluation purposes. Our implementation relies on a spatial DBMS that carries out part of the necessary analysis. Finally, through a set of experiments we demonstrate the effectiveness of the proposed approach to preserve the K-anonymity of the users for as long as the requested services are in progress. © 2010 by Nova Science Publishers, Inc. All Rights Reserved