惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

Google DeepMind News
Google DeepMind News
WordPress大学
WordPress大学
S
SegmentFault 最新的问题
小众软件
小众软件
爱范儿
爱范儿
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
量子位
博客园_首页
T
Tailwind CSS Blog
The Cloudflare Blog
J
Java Code Geeks
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
U
Unit 42
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
人人都是产品经理
人人都是产品经理
N
Netflix TechBlog - Medium
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
腾讯CDC
P
Proofpoint News Feed
aimingoo的专栏
aimingoo的专栏
Recent Announcements
Recent Announcements
T
The Blog of Author Tim Ferriss
D
Docker
Microsoft Azure Blog
Microsoft Azure Blog

cs.DC updates on arXiv.org

DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models Efficient, VRAM-Constrained xLM Inference on Clients Folding Tensor and Sequence Parallelism for Memory-Efficient Transformer Training & Inference DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training AMMA: A Multi-Chiplet Memory-Centric Architecture for Low-Latency 1M Context Attention Serving RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions PolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations ITAS: A Multi-Agent Architecture for LLM-Based Intelligent Tutoring Latency and Cost of Multi-Agent Intelligent Tutoring at Scale TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning Usable Agent Discovery for Decentralized AI Systems Cloud to Edge: Benchmarking LLM Inference On Hardware-Accelerated Single-Board Computers Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning Promoting Simple Agents: Ensemble Methods for Event-Log Prediction GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA AGNT2: Autonomous Agent Economies on Interaction-Optimized Layer 2 Infrastructure FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling A Delta-Aware Orchestration Framework for Scalable Multi-Agent Edge Computing Federated Learning over Blockchain-Enabled Cloud Infrastructure Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not? Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
Realising the Right to Data Portability for the Domestic ...
Lachlan Urquhart, Neelima Sailaja, Derek McAuley · 2018-01-23 · via cs.DC updates on arXiv.org

There is an increasing role for the IT design community to play in regulation of emerging IT. Article 25 of the EU General Data Protection Regulation (GDPR) 2016 puts this on a strict legal basis by establishing the need for information privacy by design and default (PbD) for personal data-driven technologies. Against this backdrop, we examine legal, commercial and technical perspectives around the newly created legal right to data portability (RTDP) in GDPR. We are motivated by a pressing need to address regulatory challenges stemming from the Internet of Things (IoT). We need to find channels to support the protection of these new legal rights for users in practice. In Part I we introduce the internet of things and information PbD in more detail. We briefly consider regulatory challenges posed by the IoT and the nature and practical challenges surrounding the regulatory response of information privacy by design. In Part II, we look in depth at the legal nature of the RTDP, determining what it requires from IT designers in practice but also limitations on the right and how it relates to IoT. In Part III we focus on technical approaches that can support the realisation of the right. We consider the state of the art in data management architectures, tools and platforms that can provide portability, increased transparency and user control over the data flows. In Part IV, we bring our perspectives together to reflect on the technical, legal and business barriers and opportunities that will shape the implementation of the RTDP in practice, and how the relationships may shape emerging IoT innovation and business models. We finish with brief conclusions about the future for the RTDP and PbD in the IoT.