379 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    SoK: Collusion-resistant Multi-party Private Set Intersections in the Semi-honest Model

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    Private set intersection protocols allow two parties with private sets of data to compute the intersection between them without leaking other information about their sets. These protocols have been studied for almost 20 years, and have been significantly improved over time, reducing both their computation and communication costs. However, when more than two parties want to compute a private set intersection, these protocols are no longer applicable. While extensions exist to the multi-party case, these protocols are significantly less efficient than the two-party case. It remains an open question to design collusion-resistant multi-party private set intersection (MPSI) protocols that come close to the efficiency of two-party protocols. This work is made more difficult by the immense variety in the proposed schemes and the lack of systematization. Moreover, each new work only considers a small subset of previously proposed protocols, leaving out important developments from older works. Finally, MPSI protocols rely on many possible constructions and building blocks that have not been summarized. This work aims to point protocol designers to gaps in research and promising directions, pointing out common security flaws and sketching a frame of reference. To this end, we focus on the semi-honest model. We conclude that current MPSI protocols are not a one-size-fits-all solution, and instead there exist many protocols that each prevail in their own application setting

    Regulatory technologies for the study of data and platform power in the app economy

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    Tracking, the large-scale collection of data about user behaviour, is commonplace in mobile apps. While some see tracking as a necessary evil to making apps available at lower prices by showing users personalised advertising and selling their data to third parties, tracking can also have highly disproportionate effects on the lives of individuals and society as a whole. For example, tracking has significant effects on the rights to privacy and data protection, but also on other fundamental rights, such as the right to non-discrimination (e.g. when data from mobile tracking is used in AI systems, such as targeted ads for job offers) or the right to free and fair elections (e.g. when political microtargeting is used, as in the Brexit vote or the Trump election). This thesis develops and applies techno-legal methods to study choice over app tracking at four levels: the impact of the GDPR (Chapter 4), consent to tracking in apps (Chapter 5), differences between Android and iOS (Chapters 6), and the impact of Apple’s App Tracking Transparency (ATT) framework (Chapter 7). While many previous studies looked at data protection and privacy in apps, few studies analysed tracking over time, took a compliance angle, or looked at iOS apps at scale. Throughout our analysis of apps, we find compliance problems within apps as regards key aspects of US, EU and UK data protection and privacy law, particularly the need to seek consent before tracking. For instance, while user consent is usually required prior to tracking in the EU and UK (under the ePrivacy Directive), our empirical findings suggest that tracking takes place widely and usually without users’ awareness or explicit agreement. This thesis contributes 1) a scalable downloading and analysis framework for iOS and Android privacy and compliance analysis (PlatformControl), 2) an improved understanding of the legal requirements and empirical facts regarding app tracking, 3) a comprehensive database of the relations between companies in the app ecosystem (X-Ray 2020), and 4) an Android app to support the easy and independent analysis of apps’ privacy practices (TrackerControl)

    Geographic information extraction from texts

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    A large volume of unstructured texts, containing valuable geographic information, is available online. This information – provided implicitly or explicitly – is useful not only for scientific studies (e.g., spatial humanities) but also for many practical applications (e.g., geographic information retrieval). Although large progress has been achieved in geographic information extraction from texts, there are still unsolved challenges and issues, ranging from methods, systems, and data, to applications and privacy. Therefore, this workshop will provide a timely opportunity to discuss the recent advances, new ideas, and concepts but also identify research gaps in geographic information extraction

    L4 Pointer: An efficient pointer extension for spatial memory safety support without hardware extension

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    Since buffer overflow has long been a frequently occurring, high-risk vulnerability, various methods have been developed to support spatial memory safety and prevent buffer overflow. However, every proposed method, although effective in part, has its limitations. Due to expensive bound-checking or large memory in taking for metadata, the software-only support for spatial memory safety inherently entails runtime overhead. Contrastingly, hardware-assisted methods are not available without specific hardware assistants. To mitigate such limitations, Herein we propose L4 Pointer, which is a 128-bit pointer extended from a normal 64-bit virtual addresses. By using the extra bits and widespread SIMD operations, L4 Pointer shows less slow-down and higher performance without hardware extension than existing methods

    Towards Scalable OLTP Over Fast Networks

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    Online Transaction Processing (OLTP) underpins real-time data processing in many mission-critical applications, from banking to e-commerce. These applications typically issue short-duration, latency-sensitive transactions that demand immediate processing. High-volume applications, such as Alibaba's e-commerce platform, achieve peak transaction rates as high as 70 million transactions per second, exceeding the capacity of a single machine. Instead, distributed OLTP database management systems (DBMS) are deployed across multiple powerful machines. Historically, such distributed OLTP DBMSs have been primarily designed to avoid network communication, a paradigm largely unchanged since the 1980s. However, fast networks challenge the conventional belief that network communication is the main bottleneck. In particular, emerging network technologies, like Remote Direct Memory Access (RDMA), radically alter how data can be accessed over a network. RDMA's primitives allow direct access to the memory of a remote machine within an order of magnitude of local memory access. This development invalidates the notion that network communication is the primary bottleneck. Given that traditional distributed database systems have been designed with the premise that the network is slow, they cannot efficiently exploit these fast network primitives, which requires us to reconsider how we design distributed OLTP systems. This thesis focuses on the challenges RDMA presents and its implications on the design of distributed OLTP systems. First, we examine distributed architectures to understand data access patterns and scalability in modern OLTP systems. Drawing on these insights, we advocate a distributed storage engine optimized for high-speed networks. The storage engine serves as the foundation of a database, ensuring efficient data access through three central components: indexes, synchronization primitives, and buffer management (caching). With the introduction of RDMA, the landscape of data access has undergone a significant transformation. This requires a comprehensive redesign of the storage engine components to exploit the potential of RDMA and similar high-speed network technologies. Thus, as the second contribution, we design RDMA-optimized tree-based indexes — especially applicable for disaggregated databases to access remote data efficiently. We then turn our attention to the unique challenges of RDMA. One-sided RDMA, one of the network primitives introduced by RDMA, presents a performance advantage in enabling remote memory access while bypassing the remote CPU and the operating system. This allows the remote CPU to process transactions uninterrupted, with no requirement to be on hand for network communication. However, that way, specialized one-sided RDMA synchronization primitives are required since traditional CPU-driven primitives are bypassed. We found that existing RDMA one-sided synchronization schemes are unscalable or, even worse, fail to synchronize correctly, leading to hard-to-detect data corruption. As our third contribution, we address this issue by offering guidelines to build scalable and correct one-sided RDMA synchronization primitives. Finally, recognizing that maintaining all data in memory becomes economically unattractive, we propose a distributed buffer manager design that efficiently utilizes cost-effective NVMe flash storage. By leveraging low-latency RDMA messages, our buffer manager provides a transparent memory abstraction, accessing the aggregated DRAM and NVMe storage across nodes. Central to our approach is a distributed caching protocol that dynamically caches data. With this approach, our system can outperform RDMA-enabled in-memory distributed databases while managing larger-than-memory datasets efficiently

    基于数据中台的科技型产业园区能效管理平台研究与应用

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    当前产业园区能效管理平台建设过程主要采用“烟囱式”的体系架构,存在建设成本高、重复开发、技术能力难以积累,数据不共享、可扩展性差等弊端。为此,以科技型产业园区低碳转型为切入点,通过引入数据中台概念对园区多源异构大数据进行汇聚和存储,为前端业务提供可共享复用、可快速构建的数据应用服务,探索智慧园区能效精细化管理方式。以上海市某科技型产业园区为实证,基于数据中台构建能效管理平台,依托平台积累的数据、特征算子及模型资产,驱动用电异常检测、用电时序预测等数据应用的快速构建,为园区实现节能减排、绿色发展提供坚实的数据服务支撑

    Mainstream News Articles Co-Shared with Fake News Buttress Misinformation Narratives

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    Most prior and current research examining misinformation spread on social media focuses on reports published by 'fake' news sources. These approaches fail to capture another potential form of misinformation with a much larger audience: factual news from mainstream sources ('real' news) repurposed to promote false or misleading narratives. We operationalize narratives using an existing unsupervised NLP technique and examine the narratives present in misinformation content. We find that certain articles from reliable outlets are shared by a disproportionate number of users who also shared fake news on Twitter. We consider these 'real' news articles to be co-shared with fake news. We show that co-shared articles contain existing misinformation narratives at a significantly higher rate than articles from the same reliable outlets that are not co-shared with fake news. This holds true even when articles are chosen following strict criteria of reliability for the outlets and after accounting for the alternative explanation of partisan curation of articles. For example, we observe that a recent article published by The Washington Post titled "Vaccinated people now make up a majority of COVID deaths" was disproportionately shared by Twitter users with a history of sharing anti-vaccine false news reports. Our findings suggest a strategic repurposing of mainstream news by conveyors of misinformation as a way to enhance the reach and persuasiveness of misleading narratives. We also conduct a comprehensive case study to help highlight how such repurposing can happen on Twitter as a consequence of the inclusion of particular narratives in the framing of mainstream news

    Accelerating orchestration with in-network offloading

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    The demand for low-latency Internet applications has pushed functionality that was originally placed in commodity hardware into the network. Either in the form of binaries for the programmable data plane or virtualised network functions, services are implemented within the network fabric with the aim of improving their performance and placing them close to the end user. Training of machine learning algorithms, aggregation of networking traffic, virtualised radio access components, are just some of the functions that have been deployed within the network. Therefore, as the network fabric becomes the accelerator for various applications, it is imperative that the orchestration of their components is also adapted to the constraints and capabilities of the deployment environment. This work identifies performance limitations of in-network compute use cases for both cloud and edge environments and makes suitable adaptations. Within cloud infrastructure, this thesis proposes a platform that relies on programmable switches to accelerate the performance of data replication. It then proceeds to discuss design adaptations of an orchestrator that will allow in-network data offloading and enable accelerated service deployment. At the edge, the topic of inefficient orchestration of virtualised network functions is explored, mainly with respect to energy usage and resource contention. An orchestrator is adapted to schedule requests by taking into account edge constraints in order to minimise resource contention and accelerate service processing times. With data transfers consuming valuable resources at the edge, an efficient data representation mechanism is implemented to provide statistical insight on the provenance of data at the edge and enable smart query allocation to nodes with relevant data. Taking into account the previous state of the art, the proposed data plane replication method appears to be the most computationally efficient and scalable in-network data replication platform available, with significant improvements in throughput and up to an order of magnitude decrease in latency. The orchestrator of virtual network functions at the edge was shown to reduce event rejections, total processing time, and energy consumption imbalances over the default orchestrator, thus proving more efficient use of the infrastructure. Lastly, computational cost at the edge was further reduced with the use of the proposed query allocation mechanism which minimised redundant engagement of nodes

    Toward a Bio-Inspired System Architecting Framework: Simulation of the Integration of Autonomous Bus Fleets & Alternative Fuel Infrastructures in Closed Sociotechnical Environments

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    Cities are set to become highly interconnected and coordinated environments composed of emerging technologies meant to alleviate or resolve some of the daunting issues of the 21st century such as rapid urbanization, resource scarcity, and excessive population demand in urban centers. These cybernetically-enabled built environments are expected to solve these complex problems through the use of technologies that incorporate sensors and other data collection means to fuse and understand large sums of data/information generated from other technologies and its human population. Many of these technologies will be pivotal assets in supporting and managing capabilities in various city sectors ranging from energy to healthcare. However, among these sectors, a significant amount of attention within the recent decade has been in the transportation sector due to the flood of new technological growth and cultivation, which is currently seeing extensive research, development, and even implementation of emerging technologies such as autonomous vehicles (AVs), the Internet of Things (IoT), alternative xxxvi fueling sources, clean propulsion technologies, cloud/edge computing, and many other technologies. Within the current body of knowledge, it is fairly well known how many of these emerging technologies will perform in isolation as stand-alone entities, but little is known about their performance when integrated into a transportation system with other emerging technologies and humans within the system organization. This merging of new age technologies and humans can make analyzing next generation transportation systems extremely complex to understand. Additionally, with new and alternative forms of technologies expected to come in the near-future, one can say that the quantity of technologies, especially in the smart city context, will consist of a continuously expanding array of technologies whose capabilities will increase with technological advancements, which can change the performance of a given system architecture. Therefore, the objective of this research is to understand the system architecture implications of integrating different alternative fueling infrastructures with autonomous bus (AB) fleets in the transportation system within a closed sociotechnical environment. By being able to understand the system architecture implications of alternative fueling infrastructures and AB fleets, this could provide performance-based input into a more sophisticated approach or framework which is proposed as a future work of this research
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