UPSS: a User-centric Private Storage System with its applications

• URL: http://arxiv.org/abs/2403.15884v1
• Date: Sat, 23 Mar 2024 16:35:37 GMT
• Title: UPSS: a User-centric Private Storage System with its applications
• Abstract summary: We present UPSS: the user-centric private sharing system, a storage system that can be used as a conventional or as the foundation for security-sensitive applications. We demonstrate that both the security and performance properties of UPSS exceed existing cryptographics and that its performance is comparable to mature conventionals.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Strong confidentiality, integrity, user control, reliability and performance are critical requirements in privacy-sensitive applications. Such applications would benefit from a data storage and sharing infrastructure that provides these properties even in decentralized topologies with untrusted storage backends, but users today are forced to choose between systemic security properties and system reliability or performance. As an alternative to this status quo we present UPSS: the user-centric private sharing system, a cryptographic storage system that can be used as a conventional filesystem or as the foundation for security-sensitive applications such as redaction with integrity and private revision control. We demonstrate that both the security and performance properties of UPSS exceed that of existing cryptographic filesystems and that its performance is comparable to mature conventional filesystems - in some cases, even superior. Whether used directly via its Rust API or as a conventional filesystem, UPSS provides strong security and practical performance on untrusted storage.

Related papers

• Securing Filesystems for Confidential Computing [4.044530583186265]
  Confidential computing protects applications inside Trusted Execution Environments (TEEs)<n>Even with disk encryption, a malicious cloud provider can roll back, replay, fork, or tamper with disk state.<n>We present ShieldFS, a POSIX-compliant that provides end-to-end integrity and freshness for persistent storage in the confidential-computing threat model.
  arXiv  Detail & Related papers  (2026-08-20T11:41:15Z)
• PriSrv: Privacy-Enhanced and Highly Usable Service Discovery in Wireless Communications [43.30406587083713]
  Existing protocols provide limited privacy protection, leaking sensitive device information and opening routes to network attacks.<n>This paper proposes a private service discovery protocol, called PriSrv, which enables both service providers and clients to specify fine-grained authentication policies.
  arXiv  Detail & Related papers  (2026-06-04T08:00:31Z)
• CryptoTensors: A Light-Weight Large Language Model File Format for Highly-Secure Model Distribution [16.430668737524346]
  We introduce CryptoTensors, a secure and format-compatible file structure for confidential LLM distribution.<n>Built as an extension to the widely adopted Safetensors format, CryptoTensors incorporates tensor-level encryption and embedded access control policies.<n>Our results highlight CryptoTensors as a light-weight, efficient, and developer-friendly solution for safeguarding LLM weights in real-world and widespread deployments.
  arXiv  Detail & Related papers  (2025-12-04T08:49:22Z)
• From See to Shield: ML-Assisted Fine-Grained Access Control for Visual Data [40.12543056558646]
  This work presents a system architecture for trusted data sharing with policy-driven access control.<n>The proposed architecture integrates automated detection of sensitive regions, post-correction, key management, and access control.<n>We show that our system provides effective PSO detection, increases macro-averaged F1 score (5%) and mean Average Precision (10%), and maintains an average policy-enforced decryption time of less than 1 second per image.
  arXiv  Detail & Related papers  (2025-10-22T09:41:31Z)
• Enabling Security on the Edge: A CHERI Compartmentalized Network Stack [42.78181795494584]
  CHERI provides strong security from the hardware level by enabling fine-grained compartmentalization and memory protection.<n>Our case study examines the trade-offs of isolating applications, TCP/IP libraries, and network drivers on a CheriBSD system deployed on the Arm Morello platform.
  arXiv  Detail & Related papers  (2025-07-07T09:37:59Z)
• PWC-MoE: Privacy-Aware Wireless Collaborative Mixture of Experts [59.5243730853157]
  Large language models (LLMs) hosted on cloud servers alleviate the computational and storage burdens on local devices but raise privacy concerns.<n>Small language models (SLMs) running locally enhance privacy but suffer from limited performance on complex tasks.<n>We propose a privacy-aware wireless collaborative mixture of experts (PWC-MoE) framework to balance computational cost, performance, and privacy protection under bandwidth constraints.
  arXiv  Detail & Related papers  (2025-05-13T16:27:07Z)
• Privacy-Preserving Federated Embedding Learning for Localized Retrieval-Augmented Generation [60.81109086640437]
  We propose a novel framework called Federated Retrieval-Augmented Generation (FedE4RAG) FedE4RAG facilitates collaborative training of client-side RAG retrieval models. We apply homomorphic encryption within federated learning to safeguard model parameters.
  arXiv  Detail & Related papers  (2025-04-27T04:26:02Z)
• Balancing Confidentiality and Transparency for Blockchain-based Process-Aware Information Systems [46.404531555921906]
  We propose an architecture for blockchain-based PAISs aimed at preserving both confidentiality and transparency. Smart contracts enact, enforce and store public interactions, while attribute-based encryption techniques are adopted to specify access grants to confidential information.
  arXiv  Detail & Related papers  (2024-12-07T20:18:36Z)
• Securing Legacy Communication Networks via Authenticated Cyclic Redundancy Integrity Check [98.34702864029796]
  We propose Authenticated Cyclic Redundancy Integrity Check (ACRIC) ACRIC preserves backward compatibility without requiring additional hardware and is protocol agnostic. We show that ACRIC offers robust security with minimal transmission overhead ( 1 ms)
  arXiv  Detail & Related papers  (2024-11-21T18:26:05Z)
• Lightweight, Secure and Stateful Serverless Computing with PSL [43.025002382616066]
  We present Function-as-a-Serivce (F) framework for Trusted Execution Environments (TEEs) The framework provides rich programming language support on heterogeneous TEE hardware for statically compiled binaries and/or WebAssembly (WASM) bytecodes. It achieves near-native execution speeds by utilizing the dynamic memory mapping capabilities of Intel SGX2.
  arXiv  Detail & Related papers  (2024-10-25T23:17:56Z)
• From Commands to Prompts: LLM-based Semantic File System for AIOS [46.29019415676847]
  We propose an LLM-based semantic file system ( LSFS) for prompt-driven file management. Unlike conventional approaches, LSFS incorporates LLMs to enable users or agents to interact with files through natural language prompts. Our experiments show that LSFS offers significant improvements over traditional file systems in terms of user convenience, the diversity of supported functions, and the accuracy and efficiency of file operations.
  arXiv  Detail & Related papers  (2024-09-23T08:39:16Z)
• Verifiable Decentralized IPFS Cluster: Unlocking Trustworthy Data Permanency for Off-Chain Storage [0.0]
  This paper introduces Verifiable Decentralized IPFS Clusters (VDICs) to enhance off-chain storage reliability with verifiable data permanency guarantees. Performance evaluations demonstrate that VDICs are competitive with traditional pinning services.
  arXiv  Detail & Related papers  (2024-08-09T08:26:55Z)
• Hiding Your Awful Online Choices Made More Efficient and Secure: A New Privacy-Aware Recommender System [5.397825778465797]
  This paper presents a novel privacy-aware recommender system that combines privacy-aware machine learning algorithms for practical scalability and efficiency with cryptographic primitives for solid privacy guarantees. For the first time our method makes it feasible to compute private recommendations for datasets containing 100 million entries, even on memory-constrained low-power SOC (System on Chip) devices.
  arXiv  Detail & Related papers  (2024-05-30T21:08:42Z)
• Securing the Open RAN Infrastructure: Exploring Vulnerabilities in Kubernetes Deployments [60.51751612363882]
  We investigate the security implications of and software-based Open Radio Access Network (RAN) systems. We highlight the presence of potential vulnerabilities and misconfigurations in the infrastructure supporting the Near Real-Time RAN Controller (RIC) cluster.
  arXiv  Detail & Related papers  (2024-05-03T07:18:45Z)
• FileDES: A Secure Scalable and Succinct Decentralized Encrypted Storage Network [30.775493810737192]
  Decentralized Storage Network (DSN) is an emerging technology that challenges traditional cloud-based storage systems. We propose FileDES, which incorporates three essential elements: privacy preservation, scalable storage proof, and batch verification. Our protocol outperforms the others in terms of proof generation/verification efficiency, storage costs, and scalability.
  arXiv  Detail & Related papers  (2024-03-22T06:41:30Z)
• Unified Mechanism-Specific Amplification by Subsampling and Group Privacy Amplification [54.1447806347273]
  Amplification by subsampling is one of the main primitives in machine learning with differential privacy. We propose the first general framework for deriving mechanism-specific guarantees. We analyze how subsampling affects the privacy of groups of multiple users.
  arXiv  Detail & Related papers  (2024-03-07T19:36:05Z)
• User Consented Federated Recommender System Against Personalized Attribute Inference Attack [55.24441467292359]
  We propose a user-consented federated recommendation system (UC-FedRec) to flexibly satisfy the different privacy needs of users. UC-FedRec allows users to self-define their privacy preferences to meet various demands and makes recommendations with user consent.
  arXiv  Detail & Related papers  (2023-12-23T09:44:57Z)
• SyzTrust: State-aware Fuzzing on Trusted OS Designed for IoT Devices [67.65883495888258]
  We present SyzTrust, the first state-aware fuzzing framework for vetting the security of resource-limited Trusted OSes. SyzTrust adopts a hardware-assisted framework to enable fuzzing Trusted OSes directly on IoT devices. We evaluate SyzTrust on Trusted OSes from three major vendors: Samsung, Tsinglink Cloud, and Ali Cloud.
  arXiv  Detail & Related papers  (2023-09-26T08:11:38Z)
• Blockchain-enabled Data Governance for Privacy-Preserved Sharing of Confidential Data [1.6006586061577806]
  We propose a blockchain-based data governance system that employs attribute-based encryption to prevent privacy leakage and credential misuse. First, our ABE encryption system can handle multi-authority use cases while protecting identity privacy and hiding access policy. Second, applying the Advanced Encryption Standard (AES) for data encryption makes the whole system efficient and responsive to real-world conditions.
  arXiv  Detail & Related papers  (2023-09-08T05:01:59Z)
• Securing Cloud File Systems with Trusted Execution [9.18546671155073]
  Cloud file systems have become prime targets for adversaries. New designs leveraging cryptographic techniques and trusted execution environments (TEEs) still force organizations to make undesirable trade-offs. We introduce BFS, a cloud file system that bootstraps new security protocols to deliver strong security guarantees, high-performance, and a transparent POSIX-like interface to clients.
  arXiv  Detail & Related papers  (2023-05-29T22:27:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.