23,594 research outputs found
Service Level Agreement-based GDPR Compliance and Security assurance in (multi)Cloud-based systems
Compliance with the new European General Data Protection Regulation (Regulation (EU) 2016/679) and security
assurance are currently two major challenges of Cloud-based systems. GDPR compliance implies both privacy and security
mechanisms definition, enforcement and control, including evidence collection. This paper presents a novel DevOps
framework aimed at supporting Cloud consumers in designing, deploying and operating (multi)Cloud systems that include
the necessary privacy and security controls for ensuring transparency to end-users, third parties in service provision (if any)
and law enforcement authorities. The framework relies on the risk-driven specification at design time of privacy and security
level objectives in the system Service Level Agreement (SLA) and in their continuous monitoring and enforcement at runtime.The research leading to these results has received
funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 644429
and No 780351, MUSA project and ENACT project,
respectively. We would also like to acknowledge all the
members of the MUSA Consortium and ENACT Consortium
for their valuable help
Confidential Boosting with Random Linear Classifiers for Outsourced User-generated Data
User-generated data is crucial to predictive modeling in many applications.
With a web/mobile/wearable interface, a data owner can continuously record data
generated by distributed users and build various predictive models from the
data to improve their operations, services, and revenue. Due to the large size
and evolving nature of users data, data owners may rely on public cloud service
providers (Cloud) for storage and computation scalability. Exposing sensitive
user-generated data and advanced analytic models to Cloud raises privacy
concerns. We present a confidential learning framework, SecureBoost, for data
owners that want to learn predictive models from aggregated user-generated data
but offload the storage and computational burden to Cloud without having to
worry about protecting the sensitive data. SecureBoost allows users to submit
encrypted or randomly masked data to designated Cloud directly. Our framework
utilizes random linear classifiers (RLCs) as the base classifiers in the
boosting framework to dramatically simplify the design of the proposed
confidential boosting protocols, yet still preserve the model quality. A
Cryptographic Service Provider (CSP) is used to assist the Cloud's processing,
reducing the complexity of the protocol constructions. We present two
constructions of SecureBoost: HE+GC and SecSh+GC, using combinations of
homomorphic encryption, garbled circuits, and random masking to achieve both
security and efficiency. For a boosted model, Cloud learns only the RLCs and
the CSP learns only the weights of the RLCs. Finally, the data owner collects
the two parts to get the complete model. We conduct extensive experiments to
understand the quality of the RLC-based boosting and the cost distribution of
the constructions. Our results show that SecureBoost can efficiently learn
high-quality boosting models from protected user-generated data
PRECEPT: A Framework for Ethical Digital Forensics Investigations.
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Cyber-enabled crimes are on the increase, and law enforcement has had to expand many of their detecting activities into the digital domain. As such, the field of digital forensics has become far more sophisticated over the years and is now able to uncover even more evidence that can be used to support prosecution of cyber criminals in a court of law. Governments, too, have embraced the ability to track suspicious individuals in the online world. Forensics investigators are driven to gather data exhaustively, being under pressure to provide law enforcement with sufficient evidence to secure a conviction.
Yet, there are concerns about the ethics and justice of untrammeled investigations on a number of levels. On an organizational level, unconstrained investigations could interfere with, and damage, the organization’s right to control the disclosure of their intellectual capital. On an individual level, those being investigated could easily have their legal privacy rights violated by forensics investigations. On a societal level, there might be a sense of injustice at the perceived inequality of current practice in this domain.
This paper argues the need for a practical, ethically-grounded approach to digital forensic investigations, one that acknowledges and respects the privacy rights of individuals and the intellectual capital disclosure rights of organisations, as well as acknowledging the needs of law enforcement. We derive a set of ethical guidelines, then map these onto a forensics investigation framework. We subjected the framework to expert review in two stages, refining the framework after each stage. We conclude by proposing the refined ethically-grounded digital forensics investigation framework. Our treatise is primarily UK based, but the concepts presented here have international relevance and applicability.
In this paper, the lens of justice theory is used to explore the tension that exists between the needs of digital forensic investigations into cybercrimes on the one hand, and, on the other, individuals’ rights to privacy and organizations’ rights to control intellectual capital disclosure.
The investigation revealed a potential inequality between the practices of digital forensics investigators and the rights of other stakeholders. That being so, the need for a more ethically-informed approach to digital forensics investigations, as a remedy, is highlighted, and a framework proposed to provide this.
Our proposed ethically-informed framework for guiding digital forensics investigations suggest a way of re-establishing the equality of the stakeholders in this arena, and ensuring that the potential for a sense of injustice is reduced.
Justice theory is used to highlight the difficulties in squaring the circle between the rights and expectations of all stakeholders in the digital forensics arena. The outcome is the forensics investigation guideline, PRECEpt: Privacy-Respecting EthiCal framEwork, which provides the basis for a re-aligning of the balance between the requirements and expectations of digital forensic investigators on the one hand, and individual and organizational expectations and rights, on the other
PRECEPT:a framework for ethical digital forensics investigations
Purpose: Cyber-enabled crimes are on the increase, and law enforcement has had to expand many of their detecting activities into the digital domain. As such, the field of digital forensics has become far more sophisticated over the years and is now able to uncover even more evidence that can be used to support prosecution of cyber criminals in a court of law. Governments, too, have embraced the ability to track suspicious individuals in the online world. Forensics investigators are driven to gather data exhaustively, being under pressure to provide law enforcement with sufficient evidence to secure a conviction. Yet, there are concerns about the ethics and justice of untrammeled investigations on a number of levels. On an organizational level, unconstrained investigations could interfere with, and damage, the organization’s right to control the disclosure of their intellectual capital. On an individual level, those being investigated could easily have their legal privacy rights violated by forensics investigations. On a societal level, there might be a sense of injustice at the perceived inequality of current practice in this domain. This paper argues the need for a practical, ethically-grounded approach to digital forensic investigations, one that acknowledges and respects the privacy rights of individuals and the intellectual capital disclosure rights of organisations, as well as acknowledging the needs of law enforcement. We derive a set of ethical guidelines, then map these onto a forensics investigation framework. We subjected the framework to expert review in two stages, refining the framework after each stage. We conclude by proposing the refined ethically-grounded digital forensics investigation framework. Our treatise is primarily UK based, but the concepts presented here have international relevance and applicability.Design methodology: In this paper, the lens of justice theory is used to explore the tension that exists between the needs of digital forensic investigations into cybercrimes on the one hand, and, on the other, individuals’ rights to privacy and organizations’ rights to control intellectual capital disclosure.Findings: The investigation revealed a potential inequality between the practices of digital forensics investigators and the rights of other stakeholders. That being so, the need for a more ethically-informed approach to digital forensics investigations, as a remedy, is highlighted, and a framework proposed to provide this.Practical Implications: Our proposed ethically-informed framework for guiding digital forensics investigations suggest a way of re-establishing the equality of the stakeholders in this arena, and ensuring that the potential for a sense of injustice is reduced.Originality/value: Justice theory is used to highlight the difficulties in squaring the circle between the rights and expectations of all stakeholders in the digital forensics arena. The outcome is the forensics investigation guideline, PRECEpt: Privacy-Respecting EthiCal framEwork, which provides the basis for a re-aligning of the balance between the requirements and expectations of digital forensic investigators on the one hand, and individual and organizational expectations and rights, on the other
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