Do you want to publish a course? Click here

Not fit for Purpose: A critical analysis of the Five Safes

107   0   0.0 ( 0 )
 Added by Benjamin Rubinstein
 Publication date 2020
and research's language is English




Ask ChatGPT about the research

Adopted by government agencies in Australia, New Zealand and the UK as policy instrument or as embodied into legislation, the Five Safes framework aims to manage risks of releasing data derived from personal information. Despite its popularity, the Five Safes has undergone little legal or technical critical analysis. We argue that the Fives Safes is fundamentally flawed: from being disconnected from existing legal protections and appropriation of notions of safety without providing any means to prefer strong technical measures, to viewing disclosure risk as static through time and not requiring repeat assessment. The Five Safes provides little confidence that resulting data sharing is performed using safety best practice or for purposes in service of public interest.



rate research

Read More

There is currently an increasing demand for cryptoasset analysis tools among cryptoasset service providers, the financial industry in general, as well as across academic fields. At the moment, one can choose between commercial services or low-level open-source tools providing programmatic access. In this paper, we present the design and implementation of another option: the GraphSense Cryptoasset Analytics Platform, which can be used for interactive investigations of monetary flows and, more importantly, for executing advanced analytics tasks using a standard data science tool stack. By providing a growing set of open-source components, GraphSense could ultimately become an instrument for scientific investigations in academia and a possible response to emerging compliance and regulation challenges for businesses and organizations dealing with cryptoassets.
81 - Marten Lohstroh 2017
Data security, which is concerned with the prevention of unauthorized access to computers, databases, and websites, helps protect digital privacy and ensure data integrity. It is extremely difficult, however, to make security watertight, and security breaches are not uncommon. The consequences of stolen credentials go well beyond the leakage of other types of information because they can further compromise other systems. This paper criticizes the practice of using clear-text identity attributes, such as Social Security or drivers license numbers -- which are in principle not even secret -- as acceptable authentication tokens or assertions of ownership, and proposes a simple protocol that straightforwardly applies public-key cryptography to make identity claims verifiable, even when they are issued remotely via the Internet. This protocol has the potential of elevating the business practices of credit providers, rental agencies, and other service companies that have hitherto exposed consumers to the risk of identity theft, to where identity theft becomes virtually impossible.
Insider threats are one of todays most challenging cybersecurity issues that are not well addressed by commonly employed security solutions. Despite several scientific works published in this domain, we argue that the field can benefit from the proposed structural taxonomy and novel categorization of research that contribute to the organization and disambiguation of insider threat incidents and the defense solutions used against them. The objective of our categorization is to systematize knowledge in insider threat research, while leveraging existing grounded theory method for rigorous literature review. The proposed categorization depicts the workflow among particular categories that include: 1) Incidents and datasets, 2) Analysis of attackers, 3) Simulations, and 4) Defense solutions. Special attention is paid to the definitions and taxonomies of the insider threat; we present a structural taxonomy of insider threat incidents, which is based on existing taxonomies and the 5W1H questions of the information gathering problem. Our survey will enhance researchers efforts in the domain of insider threat, because it provides: a) a novel structural taxonomy that contributes to orthogonal classification of incidents and defining the scope of defense solutions employed against them, b) an updated overview on publicly available datasets that can be used to test new detection solutions against other works, c) references of existing case studies and frameworks modeling insiders behaviors for the purpose of reviewing defense solutions or extending their coverage, and d) a discussion of existing trends and further research directions that can be used for reasoning in the insider threat domain.
135 - Peixuan Li , Danfeng Zhang 2021
Noninterference offers a rigorous end-to-end guarantee for secure propagation of information. However, real-world systems almost always involve security requirements that change during program execution, making noninterference inapplicable. Prior works alleviate the limitation to some extent, but even for a veteran in information flow security, understanding the subtleties in the syntax and semantics of each policy is challenging, largely due to very different policy specification languages, and more fundamentally, semantic requirements of each policy. We take a top-down approach and present a novel information flow policy, called Dynamic Release, which allows information flow restrictions to downgrade and upgrade in arbitrary ways. Dynamic Release is formalized on a novel framework that, for the first time, allows us to compare and contrast various dynamic policies in the literature. We show that Dynamic Release generalizes declassification, erasure, delegation and revocation. Moreover, it is the only dynamic policy that is both applicable and correct on a benchmark of tests with dynamic policy.
Atomizing various Web activities by replacing human to human interactions on the Internet has been made indispensable due to its enormous growth. However, bots also known as Web-bots which have a malicious intend and pretending to be humans pose a severe threat to various services on the Internet that implicitly assume a human interaction. Accordingly, Web service providers before allowing access to such services use various Human Interaction Proofs (HIPs) to authenticate that the user is a human and not a bot. Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) is a class of HIPs tests and are based on Artificial Intelligence. These tests are easier for humans to qualify and tough for bots to simulate. Several Web services use CAPTCHAs as a defensive mechanism against automated Web-bots. In this paper, we review the existing CAPTCHA schemes that have been proposed or are being used to protect various Web services. We classify them in groups and compare them with each other in terms of security and usability. We present general method used to generate and break text-based and image-based CAPTCHAs. Further, we discuss various security and usability issues in CAPTCHA design and provide guidelines for improving their robustness and usability.
comments
Fetching comments Fetching comments
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا