A comprehensive meta-analysis of cryptographic security mechanisms for cloud computing

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.The concept of cloud computing offers measurable computational or information resources as a service over the Internet. The major motivation behind the cloud setup is economic benefits, because it assures the reduction in expenditure for operational and infrastructural purposes. To transform it into a reality there are some impediments and hurdles which are required to be tackled, most profound of which are security, privacy and reliability issues. As the user data is revealed to the cloud, it departs the protection-sphere of the data owner. However, this brings partly new security and privacy concerns. This work focuses on these issues related to various cloud services and deployment models by spotlighting their major challenges. While the classical cryptography is an ancient discipline, modern cryptography, which has been mostly developed in the last few decades, is the subject of study which needs to be implemented so as to ensure strong security and privacy mechanisms in today’s real-world scenarios. The technological solutions, short and long term research goals of the cloud security will be described and addressed using various classical cryptographic mechanisms as well as modern ones. This work explores the new directions in cloud computing security, while highlighting the correct selection of these fundamental technologies from cryptographic point of view

Simple I/O-efficient flow accumulation on grid terrains

The flow accumulation problem for grid terrains takes as input a matrix of flow directions, that specifies for each cell of the grid to which of its eight neighbours any incoming water would flow. The problem is to compute, for each cell c, from how many cells of the terrain water would reach c. We show that this problem can be solved in O(scan(N)) I/Os for a terrain of N cells. Taking constant factors in the I/O-efficiency into account, our algorithm may be an order of magnitude faster than the previously known algorithm that is based on time-forward processing and needs O(sort(N)) I/Os.Comment: This paper is an exact copy of the paper that appeared in the abstract collection of the Workshop on Massive Data Algorithms, Aarhus, 200

Young People and Digital Intimacies. What is the evidence and what does it mean? Where next?

The digital age makes new forms of connection possible, enabling ‘digital intimacies’ including the many practices of communicating, producing and sharing intimate content (‘sexting’; selfies; making, viewing and circulating sexual content; using hook-up apps; and searching online for advice about sex). Where young people engage in digital intimacies, policymakers have tended to respond with alarm and commissioned research premised on demonstrating negative outcomes. Young people’s take up of technologies is contrasted with previous generations and ideas of ‘healthy’, ‘natural’ and ‘normal’ sexual development which ignores and marginalises diversity of sexuality and sexual expression, and leads to campaigns that seek to supervise and regulate youth sexuality. This in turn results in legislation and censorship with consequences including blocking websites for sexual abuse support and sexual education. The government has suspended introduction of Age Verification for pornographic websites but is pressing ahead with its ‘Online Harms’ White Paper which plans for broader and more comprehensive regulatory frameworks in the interests of protecting children and young people in online spaces. The UK government has positioned itself as a world leader in developing new regulatory approaches to tackle online harms but the evidence base for those approaches is neither robust nor nuanced enough to respond to the increasing mediatisation of everyday life and sexual identity. This briefing advocates for a broader recognition of young people’s investments in digital intimacies, acknowledging what growing up and learning about sex in the digital age means for young people in order to inform future policy and practice. Policies that are informed by robust research and understandings that accommodate the nuanced practices of digital intimacy will provide the support that young people need and deserve as they navigate their media lives, develop awareness of ethical and unethical behaviour, and what is right for them

Privacy-preserving scoring of tree ensembles : a novel framework for AI in healthcare

Machine Learning (ML) techniques now impact a wide variety of domains. Highly regulated industries such as healthcare and finance have stringent compliance and data governance policies around data sharing. Advances in secure multiparty computation (SMC) for privacy-preserving machine learning (PPML) can help transform these regulated industries by allowing ML computations over encrypted data with personally identifiable information (PII). Yet very little of SMC-based PPML has been put into practice so far. In this paper we present the very first framework for privacy-preserving classification of tree ensembles with application in healthcare. We first describe the underlying cryptographic protocols that enable a healthcare organization to send encrypted data securely to a ML scoring service and obtain encrypted class labels without the scoring service actually seeing that input in the clear. We then describe the deployment challenges we solved to integrate these protocols in a cloud based scalable risk-prediction platform with multiple ML models for healthcare AI. Included are system internals, and evaluations of our deployment for supporting physicians to drive better clinical outcomes in an accurate, scalable, and provably secure manner. To the best of our knowledge, this is the first such applied framework with SMC-based privacy-preserving machine learning for healthcare