Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1612.08029

Secure Cloud Storage Protocols with Data Dynamics Using Secure Network Coding Techniques

Binanda Sengupta·Akanksha Dixit·Sushmita Ruj
View original source ↗Read source document ↗Export metadata ↓

Abstract

In the age of cloud computing, cloud users with limited storage can outsource their data to remote servers. These servers, in lieu of monetary benefits, offer retrievability of their clients' data at any point of time. Secure cloud storage protocols enable a client to check integrity of outsourced data. In this work, we explore the possibility of constructing a secure cloud storage for dynamic data by leveraging the algorithms involved in secure network coding. We show that some of the secure network coding schemes can be used to construct efficient secure cloud storage protocols for dynamic data, and we construct such a protocol (DSCS I) based on a secure network coding protocol. To the best of our knowledge, DSCS I is the first secure cloud storage protocol for dynamic data constructed using secure network coding techniques which is secure in the standard model. Although generic dynamic data support arbitrary insertions, deletions and modifications, append-only data find numerous applications in the real world. We construct another secure cloud storage protocol (DSCS II) specific to append-only data -- that overcomes some limitations of DSCS I. Finally, we provide prototype implementations for DSCS I and DSCS II in order to evaluate their performance.

Explore related subjects

cs.CR

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Binanda Sengupta, Akanksha Dixit, Sushmita Ruj. 2020-06-07. Secure Cloud Storage Protocols with Data Dynamics Using Secure Network Coding Techniques. https://doi.org/10.1109/tcc.2020.3000342

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

SynGhost: Invisible and Universal Task-agnostic Backdoor Attack via Syntactic Transfer

Although pre-training achieves remarkable performance, it suffers from task-agnostic backdoor attacks due to vulnerabilities in data and training mechanisms. These attacks can transfer backdoors to various downstream tasks. In this paper, we introduce $\mathtt{maxEntropy}$, an entropy-based poisoning filter that mitigates such risks. To overcome the limitations of manual target setting and explicit triggers, we propose $\mathtt{SynGhost}$, an invisible and universal task-agnostic backdoor attack via syntactic transfer, further exposing vulnerabilities in pre-trained language models (PLMs). Specifically, $\mathtt{SynGhost}$ injects multiple syntactic backdoors into the pre-training space through corpus poisoning, while preserving the PLM's pre-training capabilities. Second, $\mathtt{SynGhost}$ adaptively selects optimal targets based on contrastive learning, creating a uniform distribution in the pre-training space. To identify syntactic differences, we also introduce an awareness module to minimize interference between backdoors. Experiments show that $\mathtt{SynGhost}$ poses significant threats and can transfer to various downstream tasks. Furthermore, $\mathtt{SynGhost}$ resists defenses based on perplexity, fine-pruning, and $\mathtt{maxEntropy}$. The code is available at https://github.com/Zhou-CyberSecurity-AI/SynGhost.

cs.CR↗
arXiv2026

Towards the ideals of Self-Recovery and Metadata Privacy in Social Vault Recovery with Apollo

Social recovery enables users to enlist trusted contacts, or trustees, to help recover lost access to end-to-end-encrypted repositories or vaults. However, existing recovery mechanisms often make strong memorability assumptions about what users will remember. Weakening these memorability assumptions to increase the robustness of recovery is possible, but may leak sensitive metadata about the user and/or trustees if done naively. This paper's first contribution is to draw attention to and formalize this basic tension between memorability and metadata privacy in social vault recovery. Our second contribution is Apollo, a social recovery mechanism that aims to avoid any memorability assumptions while strongly protecting recovery metadata privacy. Apollo approximates the ideal of self-recovery by relying only on a threshold of social reconnection events, which may be initiated either by the user or the user's contacts. Apollo thereby has a chance of succeeding in vault recovery even in a worst-case scenario where the user has forgotten all metadata, including even the vault's existence. To protect the metadata's privacy, Apollo distributes either real or fake (chaff) data to all of a user's contacts, not just the user's trustees, thus systematically anonymizing the trustees among the larger set of contacts. To make this anonymity set scalable, Apollo uses a novel multi-layered secret sharing scheme to mitigate the computational overhead of recovery in this setting, which would otherwise be exponential in the recovery threshold. Finally, we evaluate a prototype implementation of Apollo. Apollo reduces the probability of malicious recovery to under 0.1% for an adversary capable of obtaining shares from every 1-out-of-2 contacts. After reconnecting with 30 contacts, the multi-layered design shows an improvement of 5 orders in computation time, compared to a single-layered approach.

cs.CR↗
arXiv2026

Computational Certified Deletion Property of Magic Square Game and its Application to Classical Secure Key Leasing

We present the first construction of a computational Certified Deletion Property (CDP) achievable with classical communication, derived from the compilation of the non-local Magic Square Game (MSG). We leverage the KLVY compiler to transform the non-local MSG into a 2-round interactive protocol, rigorously demonstrating that this compilation preserves the game-specific CDP. Previously, the quantum value and rigidity of the compiled game were investigated. We emphasize that we are the first to investigate CDP (local randomness in [Fu and Miller, Phys. Rev. A 97, 032324 (2018)]) for the compiled game. Then, we combine this CDP with the framework [Kitagawa, Morimae, and Yamakawa, Eurocrypt 2025] to construct Secure Key Leasing with classical Lessor (cSKL). SKL enables the Lessor to lease the secret key to the Lessee and verify that a quantum Lessee has indeed deleted the key. In this paper, we realize cSKL for PKE, PRF, and digital signature. Compared to prior works for cSKL, we realize cSKL for PRF and digital signature for the first time. In addition, we succeed in weakening the assumption needed to construct cSKL.

cs.CR↗