Protection of and Access to Data under European Law

2021 ◽  
pp. 383-400
Author(s):  
Matthias Leistner
Keyword(s):  
Copyright Protection ◽  
Data Exchange ◽  
Data Access ◽  
Computer Programs ◽  
European Law ◽  
Public Sector Information ◽  
Access Problems ◽  
Eu Copyright Law ◽  
Access Rights ◽  
Access To Data

This chapter focuses on (1) keeping data exchange infrastructures freely accessible and (2) accommodating different existing or proposed access regimes with EU copyright law, in particular with sui generis protection of databases. Accordingly, the chapter mainly focuses on access issues rather than protection. Academic contributions with regard to the latter and also on necessary limitations to protection exist in abundance. By contrast, there is comparatively little literature on the different, recently proposed sector-specific access regimes and the consistent accommodation of such future access regimes with existing IP-protection. This even concerns sectors where overlap and possible contradictions are already imminent, such as the relation of EU sui generis protection for databases with the co-existing EU regime on access to public sector information. The chapter will focus on three aspects of the recent access discussion. First, free accessibility and availability of the infrastructural framework for data access, exchange, and trade in data will be discussed. Secondly, existing copyright protection of computer programs and compilations will be analysed to further identify specific access problems and hold-up potential. Thirdly, the chapter deals with EU sui generis protection for databases which, in this author’s view, is in need of imminent reform. This section also focuses on different areas and case groups, where access rights already exist or are discussed, and will categorize these access rights from an IP perspective as a sound basis for making specific reform proposals on the contextual accommodation of such future access rights with sui generis protection for databases.

Copyright in Cyberspace

2020 ◽  
pp. 191-218
Author(s):  
Mireille Hildebrandt
Keyword(s):  
Open Source ◽  
Copyright Protection ◽  
Computer Programs ◽  
Copyright Law ◽  
Free Access ◽  
Constitutional Democracy ◽  
Computer Scientists ◽  
Software Copyright ◽  
Eu Copyright Law ◽  
Ip Law

This chapter is an introduction to the domain of intellectual property (IP) rights, notably copyright. For computer scientists, the most relevant part of copyright law concerns copyright on computer programs, or software. Copyright on software is the enabling precondition for the General Public Licence (GPL) and the open source initiative. Before discussing copyright on software, however, this chapter first investigates the position of IP law in the context of constitutional democracy and clarifies that IP law is private law. From there, the chapter provides an overview of the various types of IP that are most relevant, after which it turns to the history, objectives, and scope of copyright protection. Finally, this chapter discusses EU copyright law and the issues of open source and free access.

scholarly journals Implementasi Teknik Enkripsi dan Dekripsi dI File Video Menggunakan Algoritma Blowfish

2019 ◽  
Vol 6 (6) ◽  
pp. 697
Author(s):  
Nuniek Fahriani ◽  
Harunur Rosyid
Keyword(s):  
Data Security ◽  
Data Access ◽  
Data File ◽  
Third Parties ◽  
Security System ◽  
Font Size ◽  
Video File ◽  
Encryption And Decryption ◽  
Access Rights ◽  
Access To Data

<p><strong><span style="font-family: Times New Roman; font-size: small;">Abstrak</span></strong></p><p>Kriptografi (<em>cryptography</em>) merupakan proses keamanan data untuk menjaga pesan (file) agar tidak “diganggu” oleh pihak ketiga. kriptografi memiliki unsur proses, yaitu : enkripsi, dekripsi, dan kunci. Menjadi kebutuhan <em>user</em> untuk menghindari ‘pihak ketiga’ yang bisa merubah, mengambil ataupun menghilangkan data secara fisik atau menjalankan fungsi program yang mengganggu sistem. Tingkat keaslian data menjadi bagian penting didalam sistem keamanan data. Jenis data berupa file yang berpotensi “dirusak” secara illegal tidak hanya berextention .doc bisa saja jenis file yang berextention file video. Untuk menjalankan fungsi dari sistem keamanan data file video, legalitas akses akan data sangat penting untuk<em> secure</em> sehingga tidak berakibat kepada penyalahgunaan hak akses data. Teknik yang digunakan untuk menunjang enkrip dan dekrip file video adalah menerapkan algoritma blowfish didalam implementasinya. Algoritma ini memiliki sistem keamanan yang variabel. Hasil ujicoba menggunakan 6 contoh file extention yang melalui teknik enkrip dan dekrip adalah : file extention .asf, .wmv, .avi, .3pp, .flv, .vob. Dibangun berbasis desktop.</p><p> </p><p><strong><em><span style="font-family: Times New Roman; font-size: small;">Abstract</span></em></strong></p><p><em><span style="font-family: Times New Roman; font-size: small;">Cryptography (cryptography) is a data security process to keep messages (files) from being "disturbed" by third parties. Cryptography has three basic functions, namely: encryption, decryption, and key. Being a user need to avoid 'third parties' who can change, retrieve or delete data physically or run program functions that interfere with the system. The level of authenticity of the data becomes an important part in the data security system. This type of data in the form of files that have the potential to be "tampered" illegally not only with the .doc extension can be the file type with the video file extension. To perform the function of the video file data security system, the legality of access to data is very important to secure so that it does not result in misuse of data access rights. The technique used to support encryption and decryption of video files is to apply the blowfish algorithm in its implementation. This algorithm has a variable security system. The test results using 6 sample file extensions that go through the encryption and decryption process are: file extension .asf, .wmv, .avi, .3pp, .flv, .vob. Desktop based.</span></em></p><p> </p>

DATA SHARING IN IOT ECOSYSTEMS AND COMPETITION LAW: THE EXAMPLE OF CONNECTED CARS

2019 ◽  
Vol 15 (4) ◽  
pp. 381-426 ◽  
Author(s):  
Wolfgang Kerber
Keyword(s):  
Competition Law ◽  
Data Access ◽  
Smart Device ◽  
Vehicle Data ◽  
Dominant Firms ◽  
Competition Rules ◽  
Connected Cars ◽  
Access Rights ◽  
Specific Regulation ◽  
Access To Data

ABSTRACT This paper analyzes whether competition law can help to solve problems of access to data and interoperability in Internet of Things (IoT) ecosystems, where often one firm has exclusive control of the data produced by a smart device (and of the technical access to this device). Such a gatekeeper position can lead to the elimination of competition for aftermarket and other complementary services in such IoT ecosystems. This problem is analyzed both from an economic and a legal perspective and also generally for IoT ecosystems as well as for the much discussed problems of “access to in-vehicle data and resources” in connected cars, where the “extended vehicle” concept of the car manufacturers leads to such positions of exclusive control. The paper analyzes, in particular, the competition rules about abusive behavior of dominant firms (Art. 102 TFEU) and of firms with “relative market power” (§ 20 (1) GWB) in German competition law. These provisions might offer (if appropriately applied and amended) at least some solutions for these data access problems. Competition law, however, might not be sufficient for dealing with all or most of these problems, that is, that also additional solutions might be needed (data portability, direct data (access) rights, or sector-specific regulation).

Data Inter-Operability Driven by Oceanic Data Assimilation Needs

1999 ◽  
Vol 33 (3) ◽  
pp. 55-66 ◽  
Author(s):  
L. Charles Sun
Keyword(s):  
Data Assimilation ◽  
Data Access ◽  
Heterogeneous Data ◽  
Environmental Data ◽  
Ocean Dynamics ◽  
Data Systems ◽  
Oceanographic Data ◽  
Ease Of Access ◽  
Access To Data ◽  
Station Location

An interactive data access and retrieval system, developed at the U.S. National Oceanographic Data Genter (NODG) and available at <ext-link ext-link-type="uri" href="http://www.node.noaa.gov">http://www.node.noaa.gov</ext-link>, is presented in this paper. The purposes of this paper are: (1) to illustrate the procedures of quality control and loading oceanographic data into the NODG ocean databases and (2) to describe the development of a system to manage, visualize, and disseminate the NODG data holdings over the Internet. The objective of the system is to provide ease of access to data that will be required for data assimilation models. With advances in scientific understanding of the ocean dynamics, data assimilation models require the synthesis of data from a variety of resources. Modern intelligent data systems usually involve integrating distributed heterogeneous data and information sources. As the repository for oceanographic data, NOAA’s National Oceanographic Data Genter (NODG) is in a unique position to develop such a data system. In support of the data assimilation needs, NODG has developed a system to facilitate browsing of the oceanographic environmental data and information that is available on-line at NODG. Users may select oceanographic data based on geographic areas, time periods and measured parameters. Once the selection is complete, users may produce a station location plot, produce plots of the parameters or retrieve the data.

scholarly journals Implementasi Virtual Interface Menggunakan Metode EOIP Tunnel Pada Jaringan WAN PT. Indo Matra Lestari

2020 ◽  
Vol 6 (1) ◽  
pp. 103-110
Author(s):  
Sidik Sidik ◽  
Ade Sudaryana ◽  
Rame Santoso
Keyword(s):  
Data Center ◽  
Data Access ◽  
Local Network ◽  
Virtual Interface ◽  
Head Office ◽  
Secure Network ◽  
Pass Through ◽  
Point To Point ◽  
Access To Data ◽  
Branch Office

Computer networks have become an important point in companies that have many branch offices to coordinate the transfer of data. PT Indo Matra Lestari's connection uses a VPN system using the PPTP method. Data Center is used as a VPN server, the client is the Head Office and Citereup Branch Offices. Between the Head Office and the Citereup Branch Office there is no direct connection so access to data made between the Head Office and the Citereup Branch Office is slow, because the data must pass through the Data Center before reaching its destination. Moreover, the data accessed is private to the company and only accessed on the local network. The solution used to create a direct and secure network path between the Head Office and Branch Offices is to use the EoIP Tunnel on the proxy router. Tunneling method in EoIP can make network bridging between proxy devices, EoIP Tunnel will change to Virtual Interface on the proxy router so that it is as if the proxy router is connected locally. Tunnel ID on the EoIP Tunnel functions as a tunneling path security. The application of the EoIP Tunnel makes the point to point connection point between Mikrotik devices faster in data access because the data access is directed to the destination. In order for this EoIP Tunnel connection to run optimally and well, a network management is needed in managing internet bandwidth usage

scholarly journals British Columbia’s Health Data Platform: Unleashing the Power of a Data Environment Commons for Health and Health System Improvement

2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Shirley Wong ◽  
Victoria Schuckel ◽  
Simon Thompson ◽  
David Ford ◽  
Ronan Lyons ◽  
...  
Keyword(s):  
Health Sector ◽  
Data Access ◽  
Health Data ◽  
Decision Makers ◽  
Security And Privacy ◽  
Privacy And Security ◽  
Data Accessibility ◽  
Data Platform ◽  
Access To Data ◽  
Data Consumer

IntroductionThere is no power for change greater than a community discovering what it cares about.1 The Health Data Platform (HDP) will democratize British Columbia’s (population of approximately 4.6 million) health sector data by creating common enabling infrastructure that supports cross-organization analytics and research used by both decision makers and cademics. HDP will provide streamlined, proportionate processes that provide timelier access to data with increased transparency for the data consumer and provide shared data related services that elevate best practices by enabling consistency across data contributors, while maintaining continued stewardship of their data. HDP will be built in collaboration with Swansea University following an agile pragmatic approach starting with a minimum viable product. Objectives and ApproachBuild a data sharing environment that harnesses the data and the understanding and expertise about health data across academe, decision makers, and clinicians in the province by: Enabling a common harmonized approach across the sector on: Data stewardship Data access Data security and privacy Data management Data standards To: Enhance data consumer data access experience Increase process consistency and transparency Reduce burden of liberating data from a data source Build trust in the data and what it is telling us and therefore the decisions made Increase data accessibility safely and responsibly Working within the jurisdiction’s existing legislation, the Five Safes Privacy and Security Framework will be implemented, tailored to address the requirements of data contributors. ResultsThe minimum viable product will provide the necessary enabling infrastructure including governance to enable timelier access, safely to administrative data to a limited set of data consumers. The MVP will be expanded with another release planned for early 2021. Conclusion / ImplicationsCollaboration with Swansea University has enabled BC to accelerate its journey to increasing timelier access to data, safely and increasing the maturity of analytics by creating the enabling infrastructure that promotes collaboration and sharing of data and data approaches. 1 Margaret Wheatley

scholarly journals RESOURCES PROVIDING DATA FOR MACHINE LEARNING AND TESTING ARTIFICIAL INTELLIGENCE TECHNOLOGIES

2021 ◽  
pp. 39-52
Author(s):  
Денис Валерьевич Сикулер
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
World Bank ◽  
Open Data ◽  
Data Access ◽  
Free Access ◽  
Internet Resources ◽  
Data Registry ◽  
Data Portal ◽  
Access To Data

В статье выполнен обзор 10 ресурсов сети Интернет, позволяющих подобрать данные для разнообразных задач, связанных с машинным обучением и искусственным интеллектом. Рассмотрены как широко известные сайты (например, Kaggle, Registry of Open Data on AWS), так и менее популярные или узкоспециализированные ресурсы (к примеру, The Big Bad NLP Database, Common Crawl). Все ресурсы предоставляют бесплатный доступ к данным, в большинстве случаев для этого даже не требуется регистрация. Для каждого ресурса указаны характеристики и особенности, касающиеся поиска и получения наборов данных. В работе представлены следующие сайты: Kaggle, Google Research, Microsoft Research Open Data, Registry of Open Data on AWS, Harvard Dataverse Repository, Zenodo, Портал открытых данных Российской Федерации, World Bank, The Big Bad NLP Database, Common Crawl. The work presents review of 10 Internet resources that can be used to find data for different tasks related to machine learning and artificial intelligence. There were examined some popular sites (like Kaggle, Registry of Open Data on AWS) and some less known and specific ones (like The Big Bad NLP Database, Common Crawl). All included resources provide free access to data. Moreover in most cases registration is not needed for data access. Main features are specified for every examined resource, including regarding data search and access. The following sites are included in the review: Kaggle, Google Research, Microsoft Research Open Data, Registry of Open Data on AWS, Harvard Dataverse Repository, Zenodo, Open Data portal of the Russian Federation, World Bank, The Big Bad NLP Database, Common Crawl.

Contributions of Information Science in the Data Science Era

2021 ◽  
pp. 45-64
Author(s):  
Rogério Aparecido Sá Ramalho ◽  
Ricardo César Gonçalves Sant'Ana ◽  
Francisco Carlos Paletta
Keyword(s):  
Life Cycle ◽  
Data Science ◽  
Information Science ◽  
Theoretical Study ◽  
Digital Technologies ◽  
Data Access ◽  
New Challenges ◽  
Object Of Study ◽  
Access To Data ◽  
The Relationship

The acceleration of the development of digital technologies and the increase of the capillarity of their effects present new challenges to the praxis related to the treatment and informational flows and those that are object of study of information science. This chapter is based on a theoretical study that analyzes information science contributions in the data science era, analyzing from the Cynefin Framework to the new contemporary informational demands generated by the increasing predominance of data access and use. In order to establish the relationship between the skills expected from the information science professional and its relationship with access to data, the Cynefin Framework was used as a basis to establish a perspective of analyzing the skills involved in each of the phases of the life cycle of the data.

Sign in / Sign up

Export Citation Format

Share Document