Security of Data Science and Data Science for Security

Tellenbach, Bernhard; Rennhard, Marc; Schweizer, Remo

doi:10.1007/978-3-030-11821-1_15

Cited by 3 publications

(1 citation statement)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data security is another issue outside the ordinary context of information security fostered by infrastructural components. However, data scientists with security skills can identify anomalies in data that alert them of falsifications [3]. Such trends can protect data scientists from the risk of using false information that should also alert them of a security challenge in their information systems.…”

Section: Security Risks In Data Sciencementioning

confidence: 99%

Trusted Computing in Data Science: Viable Countermeasure in Risk Management Plan

Emejeamara¹,

Nwoduh²,

Madu³

2020

Computer Science &Amp; Information Technology

View full text Add to dashboard Cite

The need for secure data systems has prompted, the constant reinforcement of security systems in the attempt to prevent and mitigate risks associated with information security. The purpose of this paper is to examine the effectiveness of trusted computing in data science as a countermeasure in risk management planning. In the information age, it is evident that companies cannot ignore the impact of data, specifically big data, in the decision making processes. It promotes not only the proactive capacity to prevent unwarranted situations while exploiting opportunities but also the keeping up of the pace of market competition. However, since the overreliance on data exposes the company, trusted computing components are necessary to guarantee that data acquired, stored, and processed remains secure from internal and external malice. Numerous measures can be adopted to counter the risks associated with data exploitation and exposure due to data science practices. Nonetheless, trusted computing is a reasonable point to begin with, in the aim to protect provenance systems and big data systems through the establishment of a 'chain of trust' among the various computing components and platforms. The research reveals that trusted computing is most effective when combined with other hardware-based security solutions since attack vectors can follow diverse paths. The results demonstrate the potential that the technology provides for application in risk management.

show abstract

Section: Security Risks In Data Sciencementioning

confidence: 99%

Trusted Computing in Data Science: Viable Countermeasure in Risk Management Plan

Emejeamara¹,

Nwoduh²,

Madu³

2020

Computer Science &Amp; Information Technology

View full text Add to dashboard Cite

show abstract

SEAL: Capability-Based Access Control for Data-Analytic Scenarios

Rasifard

Gopinath

Backes

et al. 2023

Proceedings of the 28th ACM Symposium on Access Control Models and Technologies

View full text Add to dashboard Cite

Open Scientific Data: Analytics of European and National Standards for Quality, Interoperability and Safety

Rudakova,

Shapovalov,

Kuznetsova

et al. 2023

Scientific Notes of Junior Academy of Sciences of Ukraine

View full text Add to dashboard Cite

The article analyzes European and national standards regarding the quality, interoperability and security of data in the context of open scientific data. Today, in the European Union, there are progressive developments at the legislative level regarding the quality, interoperability and security of open data, in particular scientific data — these are ISO Standards, Directives, Recommendations, Guidelines, Regulations, etc. However, national legislative norms mostly concern the field of interoperability and security of open data. Regarding data interoperability, the Law of Ukraine “On Access to Public Information” provides for public information to be provided in the form of open data by default, while research data (open scientific data) does not belong to its scope. To ensure management, information security and information protection, the government plans to implement ISO 20000, ISO 27001 standards in the field of research infrastructure management and information protection in the field of research. It should be noted that in the decree of the Cabinet of Ministers of Ukraine No. 892‑r “On the approval of the national plan for open science”, as in other resolutions, the main emphasis is placed on the interoperability and security of open scientific data. On the basis of a comparison of the European and national regulatory framework, we proposed to make changes to: Laws of Ukraine “On access to public information”, in particular, on access to research data, “On information”, “On scientific and technical information”, “On scientific and scientific — technical activity”, in particular, that all scientific publications obtained as a result of state-funded research should be publicly available. In addition, we emphasize the implementation of the ISO/IEC 25012:2008 “Data Quality Model” standard and a practical guide to obtaining quality data.

show abstract

Security of Data Science and Data Science for Security

Cited by 3 publications

References 33 publications

Trusted Computing in Data Science: Viable Countermeasure in Risk Management Plan

Trusted Computing in Data Science: Viable Countermeasure in Risk Management Plan

SEAL: Capability-Based Access Control for Data-Analytic Scenarios

Open Scientific Data: Analytics of European and National Standards for Quality, Interoperability and Safety

Contact Info

Product

Resources

About