Secure Virtualization and Multicore Platforms State-of-the-Art report

SVaM

SplitFS: Reducing Software Overhead in File Systems for Persistent Memory

We present SplitFS, a file system for persistent memory (PM) that reduces software overhead significantly compared to state-of-the-art PM file systems. SplitFS presents a novel split of responsibilities between a user-space library file system and an existing kernel PM file system. The user-space library file system handles data operations by intercepting POSIX calls, memory-mapping the underlying file, and serving the read and overwrites using processor loads and stores. Metadata operations are handled by the kernel PM file system (ext4 DAX). SplitFS introduces a new primitive termed relink to efficiently support file appends and atomic data operations. SplitFS provides three consistency modes, which different applications can choose from, without interfering with each other. SplitFS reduces software overhead by up-to 4x compared to the NOVA PM file system, and 17x compared to ext4-DAX. On a number of micro-benchmarks and applications such as the LevelDB key-value store running the YCSB benchmark, SplitFS increases application performance by up to 2x compared to ext4 DAX and NOVA while providing similar consistency guarantees

Understanding Concurrency Vulnerabilities in Linux Kernel

While there is a large body of work on analyzing concurrency related software bugs and developing techniques for detecting and patching them, little attention has been given to concurrency related security vulnerabilities. The two are different in that not all bugs are vulnerabilities: for a bug to be exploitable, there needs be a way for attackers to trigger its execution and cause damage, e.g., by revealing sensitive data or running malicious code. To fill the gap, we conduct the first empirical study of concurrency vulnerabilities reported in the Linux operating system in the past ten years. We focus on analyzing the confirmed vulnerabilities archived in the Common Vulnerabilities and Exposures (CVE) database, which are then categorized into different groups based on bug types, exploit patterns, and patch strategies adopted by developers. We use code snippets to illustrate individual vulnerability types and patch strategies. We also use statistics to illustrate the entire landscape, including the percentage of each vulnerability type. We hope to shed some light on the problem, e.g., concurrency vulnerabilities continue to pose a serious threat to system security, and it is difficult even for kernel developers to analyze and patch them. Therefore, more efforts are needed to develop tools and techniques for analyzing and patching these vulnerabilities.Comment: It was finished in Oct 201

Security Issues in Data Warehouse

Data Warehouse (DWH) provides storage for huge amounts of historical data from heterogeneous operational sources in the form of multidimensional views, thus supplying sensitive and useful information which help decision-makers to improve the organization’s business processes. A data warehouse environment must ensure that data collected and stored in one big repository are not vulnerable. A review of security approaches specifically for data warehouse environment and issues concerning each type of security approach have been provided in this paper

A CleanRoom approach to bring your own apps

Today, on mobile devices such as smartphones and tablets, hundreds of thousands of software apps provide useful services to users. Users use these apps to search and browse the web, perform financial transactions, emailing, among other functions. Besides, these apps use cloud services which gives the users the flexibility to access them from anywhere and from any device. Because of the rich functionality of these apps and ease of use of mobile devices, users (employees) often want to use their devices and preferred apps at their workplace. However, these apps not only pose risk to user's private data but also to enterprise data, when users use them within an enterprise network. For one thing, these apps come from hundreds and thousands of different app publishers, where all of them may not be trustworthy. Second, apps often need user's private data such as location, contact list, photos among others and use remote cloud to carry out their operations. In the process apps may leak a user's private or enterprise confidential data to a third party. Current practices to prevent such leaks through user enabled app permissions fall short because often user does not understand these permissions. Besides, even if a company's "Bring Your Own Device" (BYOD) policies mitigate the risk of device compromise with enterprise-approved password policies, remote wipe capabilities, and OS security upgrade policies, the apps on those devices pose their own risks. This thesis presents CleanRoom, a new app platform that prevents apps from leaking the data entrusted to them. It does not rely on users to make good decisions about Privacy, and enables enterprises to allow its employees to use their own devices and bring their preferred apps to work.Computer Science

Information access tasks and evaluation for personal lifelogs

Emerging personal lifelog (PL) collections contain permanent digital records of information associated with individuals’ daily lives. This can include materials such as emails received and sent, web content and other documents with which they have interacted, photographs, videos and music experienced passively or created, logs of phone calls and text messages, and also personal and contextual data such as location (e.g. via GPS sensors), persons and objects present (e.g. via Bluetooth) and physiological state (e.g. via biometric sensors). PLs can be collected by individuals over very extended periods, potentially running to many years. Such archives have many potential applications including helping individuals recover partial forgotten information, sharing experiences with friends or family, telling the story of one’s life, clinical applications for the memory impaired, and fundamental psychological investigations of memory. The Centre for Digital Video Processing (CDVP) at Dublin City University is currently engaged in the collection and exploration of applications of large PLs. We are collecting rich archives of daily life including textual and visual materials, and contextual context data. An important part of this work is to consider how the effectiveness of our ideas can be measured in terms of metrics and experimental design. While these studies have considerable similarity with traditional evaluation activities in areas such as information retrieval and summarization, the characteristics of PLs mean that new challenges and questions emerge. We are currently exploring the issues through a series of pilot studies and questionnaires. Our initial results indicate that there are many research questions to be explored and that the relationships between personal memory, context and content for these tasks is complex and fascinating