Data Value Estimation for Privacy-Preserving Big/Personal Data Businesses

2015 ◽  
pp. 149-158
Author(s):  
Shinsaku Kiyomoto
Keyword(s):  
Personal Data ◽  
Privacy Preserving ◽  
Data Value ◽  
Value Estimation

Mutual Correlation-Based Anonymization for Privacy Preserving Medical Data Publishing

2018 ◽  
pp. 304-319
Author(s):  
Ashoka Kukkuvada ◽  
Poornima Basavaraju
Keyword(s):  
Privacy Preservation ◽  
Information Gain ◽  
Personal Data ◽  
Privacy Preserving ◽  
Medical Data ◽  
Innovative Approach ◽  
Data Publishing ◽  
Mutual Correlation ◽  
Data Utility ◽  
Data Elements

Currently the industry is focused on managing, retrieving, and securing massive amounts of data. Hence, privacy preservation is a significant concern for those organizations that publish/share personal data for vernacular analysis. In this chapter, the authors presented an innovative approach that makes use of information gain of the quasi attributes with respect to sensitive attributes for anonymizing the data, which gives the fruitfulness of an attribute in classifying the data elements, which is a two-way correlation among attributes. The authors show that the proposed approach preserves better data utility and has lesser complexity than former methods.

scholarly journals Privacy-Preserving Process Mining in Healthcare

2020 ◽  
Vol 17 (5) ◽  
pp. 1612 ◽  
Author(s):  
Anastasiia Pika ◽  
Moe T. Wynn ◽  
Stephanus Budiono ◽  
Arthur H.M. ter Hofstede ◽  
Wil M.P. van der Aalst ◽  
...  
Keyword(s):  
Data Mining ◽  
Data Privacy ◽  
Process Mining ◽  
Personal Data ◽  
Data Transformation ◽  
Privacy Preserving ◽  
Sensitive Information ◽  
Process Data ◽  
Mining Community ◽  
Healthcare Process

Process mining has been successfully applied in the healthcare domain and has helped to uncover various insights for improving healthcare processes. While the benefits of process mining are widely acknowledged, many people rightfully have concerns about irresponsible uses of personal data. Healthcare information systems contain highly sensitive information and healthcare regulations often require protection of data privacy. The need to comply with strict privacy requirements may result in a decreased data utility for analysis. Until recently, data privacy issues did not get much attention in the process mining community; however, several privacy-preserving data transformation techniques have been proposed in the data mining community. Many similarities between data mining and process mining exist, but there are key differences that make privacy-preserving data mining techniques unsuitable to anonymise process data (without adaptations). In this article, we analyse data privacy and utility requirements for healthcare process data and assess the suitability of privacy-preserving data transformation methods to anonymise healthcare data. We demonstrate how some of these anonymisation methods affect various process mining results using three publicly available healthcare event logs. We describe a framework for privacy-preserving process mining that can support healthcare process mining analyses. We also advocate the recording of privacy metadata to capture information about privacy-preserving transformations performed on an event log.

Privacy Preserving Big Data Publishing

2021 ◽  
pp. 1281-1298
Author(s):  
Nancy Victor ◽  
Daphne Lopez
Keyword(s):  
Big Data ◽  
Language Processing ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Personal Data ◽  
Privacy Preserving ◽  
Data Publishing ◽  
Time Data ◽  
Digital World ◽  
Distributed Framework

Data privacy plays a noteworthy part in today's digital world where information is gathered at exceptional rates from different sources. Privacy preserving data publishing refers to the process of publishing personal data without questioning the privacy of individuals in any manner. A variety of approaches have been devised to forfend consumer privacy by applying traditional anonymization mechanisms. But these mechanisms are not well suited for Big Data, as the data which is generated nowadays is not just structured in manner. The data which is generated at very high velocities from various sources includes unstructured and semi-structured information, and thus becomes very difficult to process using traditional mechanisms. This chapter focuses on the various challenges with Big Data, PPDM and PPDP techniques for Big Data and how well it can be scaled for processing both historical and real-time data together using Lambda architecture. A distributed framework for privacy preservation in Big Data by combining Natural language processing techniques is also proposed in this chapter.

scholarly journals The effect of a blockchain-supported, privacy-preserving system on disclosure of personal data

2017 ◽  
Author(s):  
Remo Manuel Frey ◽  
Pascal Buhler ◽  
Alexander Gerdes ◽  
Thomas Hardjono ◽  
Klaus Ludwig Fuchs ◽  
...  
Keyword(s):  
Personal Data ◽  
Privacy Preserving

scholarly journals Reporting, recording, and communication of COVID-19 cases in workplace: data protection as a moving target

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Mahsa Shabani ◽  
Tom Goffin ◽  
Heidi Mertes
Keyword(s):  
Data Collection ◽  
Data Protection ◽  
Personal Data ◽  
Privacy Preserving ◽  
Moving Target ◽  
National Data ◽  
Risks And Benefits ◽  
Guidelines And Recommendations

Abstract In response to concerns related to privacy in the context of coronavirus disease 2019 (COVID-19), recently European and national Data Protection Authorities (DPAs) issued guidelines and recommendations addressing a variety of issues related to the processing of personal data for preventive purposes. One of the recurring questions in these guidelines is related to the rights and responsibilities of employers and employees in reporting, recording, and communicating COVID-19 cases in workplace. National DPAs in some cases adopted different approaches regarding duties in reporting and communicating the COVID-19 cases; however, they unanimously stressed the importance of adopting privacy-preserving approaches to avoid raising concerns about surveillance and stigmatization. We stress that in view of the increasing use of new data collection and sharing tools such as ‘tracing and warning’ apps, the associated privacy-related risks should be evaluated on an ongoing manner. In addition, the intricacies of different settings where such apps may be used should be taken into consideration when assessing the associated risks and benefits.

scholarly journals The euBusinessGraph ontology: A lightweight ontology for harmonizing basic company information

Semantic Web ◽  
2021 ◽  
pp. 1-28
Author(s):  
Dumitru Roman ◽  
Vladimir Alexiev ◽  
Javier Paniagua ◽  
Brian Elvesæter ◽  
Bjørn Marius von Zernichow ◽  
...  
Keyword(s):  
Development Process ◽  
Personal Data ◽  
Ontology Development ◽  
Value Chains ◽  
Knowledge Graph ◽  
Balance Sheets ◽  
Business Information ◽  
Data Value ◽  
Core Concepts ◽  
National Business

Company data, ranging from basic company information such as company name(s) and incorporation date to complex balance sheets and personal data about directors and shareholders, are the foundation that many data value chains depend upon in various sectors (e.g., business information, marketing and sales, etc.). Company data becomes a valuable asset when data is collected and integrated from a variety of sources, both authoritative (e.g., national business registers) and non-authoritative (e.g., company websites). Company data integration is however a difficult task primarily due to the heterogeneity and complexity of company data, and the lack of generally agreed upon semantic descriptions of the concepts in this domain. In this article, we introduce the euBusinessGraph ontology as a lightweight mechanism for harmonising company data for the purpose of aggregating, linking, provisioning and analysing basic company data. The article provides an overview of the related work, ontology scope, ontology development process, explanations of core concepts and relationships, and the implementation of the ontology. Furthermore, we present scenarios where the ontology was used, among others, for publishing company data (business knowledge graph) and for comparing data from various company data providers. The euBusinessGraph ontology serves as an asset not only for enabling various tasks related to company data but also on which various extensions can be built upon.

scholarly journals Optimization of the Mainzelliste software for fast privacy-preserving record linkage

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Florens Rohde ◽  
Martin Franke ◽  
Ziad Sehili ◽  
Martin Lablans ◽  
Erhard Rahm
Keyword(s):  
Record Linkage ◽  
Comprehensive Evaluation ◽  
Personal Data ◽  
Privacy Preserving ◽  
Use Cases ◽  
Locality Sensitive Hashing ◽  
Third Party ◽  
Bloom Filters ◽  
Linkage Quality ◽  
Order Of Magnitude

Abstract Background Data analysis for biomedical research often requires a record linkage step to identify records from multiple data sources referring to the same person. Due to the lack of unique personal identifiers across these sources, record linkage relies on the similarity of personal data such as first and last names or birth dates. However, the exchange of such identifying data with a third party, as is the case in record linkage, is generally subject to strict privacy requirements. This problem is addressed by privacy-preserving record linkage (PPRL) and pseudonymization services. Mainzelliste is an open-source record linkage and pseudonymization service used to carry out PPRL processes in real-world use cases. Methods We evaluate the linkage quality and performance of the linkage process using several real and near-real datasets with different properties w.r.t. size and error-rate of matching records. We conduct a comparison between (plaintext) record linkage and PPRL based on encoded records (Bloom filters). Furthermore, since the Mainzelliste software offers no blocking mechanism, we extend it by phonetic blocking as well as novel blocking schemes based on locality-sensitive hashing (LSH) to improve runtime for both standard and privacy-preserving record linkage. Results The Mainzelliste achieves high linkage quality for PPRL using field-level Bloom filters due to the use of an error-tolerant matching algorithm that can handle variances in names, in particular missing or transposed name compounds. However, due to the absence of blocking, the runtimes are unacceptable for real use cases with larger datasets. The newly implemented blocking approaches improve runtimes by orders of magnitude while retaining high linkage quality. Conclusion We conduct the first comprehensive evaluation of the record linkage facilities of the Mainzelliste software and extend it with blocking methods to improve its runtime. We observed a very high linkage quality for both plaintext as well as encoded data even in the presence of errors. The provided blocking methods provide order of magnitude improvements regarding runtime performance thus facilitating the use in research projects with large datasets and many participants.

scholarly journals Personal data utilization and protection through algorithms and protocols for privacy-preserving electronic transactions

2015 ◽  
Author(s):  
Αιμιλία Τασίδου
Keyword(s):  
Personal Data ◽  
Privacy Preserving ◽  
Data Utilization ◽  
Electronic Transactions ◽  
Economics Of Privacy

Ο τρόπος που διεξάγονται οι ηλεκτρονικές συναλλαγές σήμερα αναγκάζει τους πολίτες να παραχωρούν τα προσωπικά τους δεδομένα σε εταιρείες και φορείς, χωρίς να γνωρίζουν πώς αυτά θα χρησιμοποιηθούν στην συνέχεια. Περιστατικά σκόπιμης ή μη διαρροής προσωπικών δεδομένων έρχονται στο φως της δημοσιότητας καθημερινά, προκαλώντας την έντονη ανησυχία των πολιτών. Η ψηφιοποίηση πολλών δραστηριοτήτων της καθημερινότητας του σύγχρονου ατόμου, οι αυξημένες δυνατότητες αποθήκευσης μεγάλου όγκου δεδομένων και τα ισχυρά μέσα επεξεργασίας που υπάρχουν διαθέσιμα σήμερα, καθιστούν τη διαρροή προσωπικών δεδομένων σημαντική απειλή για την ασφάλεια και την ευημερία των πολιτών. Η έλλειψη προστασίας της ιδιωτικότητας και οι αρνητικές συνέπειες που προκύπτουν από την καταχρηστική εκμετάλλευση των προσωπικών δεδομένων, απασχολεί όλο και περισσότερο τους σύγχρονους πολίτες, οι οποίοι νιώθουν ανίκανοι να ελέγξουν πού διακινούνται και με ποιόν τρόπο χρησιμοποιούνται τα προσωπικά τους δεδομένα. Το γεγονός αυτό έχει άμεσες επιπτώσεις στην εμπιστοσύνη των πολιτών προς τις τεχνολογίες της Πληροφορικής και αποτελεί εμπόδιο στην ανάπτυξη και διάδοση εφαρμογών ηλεκτρονικών δραστηριοτήτων.Από την άλλη πλευρά, οι δυνατότητες μαζικής συλλογής προσωπικών δεδομένων από τις όλο και αυξανόμενες ηλεκτρονικές δραστηριότητες των πολιτών, ανοίγει νέες ευκαιρίες αξιοποίησης των δεδομένων αυτών, τόσο προς όφελος των εταιρειών, όσο και προς όφελος του ίδιου του πολίτη, ο οποίος έχει πλέον την δυνατότητα να μελετήσει την ίδια του τη συμπεριφορά και τις συνήθειές του και να εξάγει χρήσιμα συμπεράσματα από τα δεδομένα αυτά. Επιπλέον, η δυνατότητα χρήσης των δεδομένων των χρηστών για την αποτελεσματικότερη εξατομίκευση των υπηρεσιών που τους παρέχονται αποτελεί σημαντικό παράγοντα βελτίωσης των παρεχόμενων υπηρεσιών σήμερα.Για τους παραπάνω λόγους, καθίσταται κρίσιμη η ανάπτυξη σύγχρονων συστημάτων προστασίας της ιδιωτικότητας, τα οποία θα ενισχύουν την σωστότερη και αποδοτικότερη αξιοποίηση των προσωπικών δεδομένων, μειώνοντας τις πιθανότητες διαρροής και των κινδύνων που αυτή ενέχει.Η οικονομική διάσταση της ιδιωτικότητας (Economics of Privacy) αποτελεί ένα σύγχρονο και ιδιαίτερα επίκαιρο αντικείμενο έρευνας. Η επεξεργασία προσωπικών δεδομένων επιφέρει σημαντικά κέρδη στην αγορά σήμερα, αλλά ταυτόχρονα κρύβει σημαντικούς κινδύνους για την ιδιωτικότητα των πολιτών, με σημαντικά κόστη, σχετικά τόσο με την ασφάλεια όσο και με την οικονομία. Ταυτόχρονα, ο πολίτης δεν λαμβάνει κανένα μέρος από τα κέρδη των εταιρειών από την εκμετάλλευση των προσωπικών του δεδομένων. Για τους παραπάνω λόγους, ενισχύεται η ιδέα ότι ο πολίτης έχει το δικαίωμα να γνωρίζει και να ελέγχει τους αποδέκτες και τις χρήσεις των προσωπικών του δεδομένων, καθώς και να αποζημιώνεται για την χρήση τους. Η παρούσα διδακτορική έρευνα περιλαμβάνει τη διερεύνηση των τρόπων αξιοποίησης και προστασίας προσωπικών δεδομένων προς όφελος των κατόχων τους, προσφέροντάς τους τον έλεγχο της διακίνησης των προσωπικών τους πληροφοριών, καθώς και οφέλη από την χρήση τους στις συναλλαγές τους. Βασική αρχή της έρευνας είναι η ιδέα ότι τα προσωπικά δεδομένα των πολιτών οφείλουν να παραμένουν στον έλεγχο των ίδιων των κατόχων τους, με συνέπεια αυτοί να παραμένουν ενήμεροι για τους σκοπούς, αλλά και για τους τρόπους που αυτά χρησιμοποιούνται. Στόχος είναι η δημιουργία ενός δίκαιου, ασφαλούς και νόμιμου τρόπου απόκτησης πρόσβασης και αξιοποίησης των προσωπικών δεδομένων των πολιτών. Άλλωστε, ο σκοπός της προστασίας της ιδιωτικότητας δεν είναι να αποκλειστεί η πρόσβαση σε κάθε είδους προσωπικά δεδομένα, αλλά να επιτευχθεί η πρόσβαση στα κατάλληλα δεδομένα με ελεγχόμενο τρόπο.

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