2026· Proceedings of the 23rd International Conference on Security and Cryptography· pp. 637-648· 0 citations· 21 references
TL;DR
A privacy model for searchable symmetric encryption protocols that makes adversarial power a central parameter and induces four privacy levels giving rise to a privacy lattice is proposed, enabling reasoning about how privacy guarantees change under different adversarial capabilities.
Abstract
: Searchable symmetric encryption enables efficient keyword search over encrypted outsourced data, making it a key primitive for cloud storage. Since searchable symmetric encryption protocols inevitably leak side information through repeated searches, access patterns, update behaviour, and timing, an analysis of their privacy properties is crucial. Prior work along this direction considers leakage functions capturing what is revealed, and privacy guarantees often remain difficult to compare across schemes. We propose a privacy model for searchable symmetric encryption protocols that makes adversarial power a central parameter. We formalise adversaries capabilities and organise them into four classes based on two orthogonal dimensions, observational power (low vs. high) and privacy guarantee strength (minimal vs. strong). These classes induce four privacy levels giving rise to a privacy lattice, capturing implication relations between guarantees under increasingly powerful adversaries. This enables reasoning about how privacy guarantees change under different adversarial capabilities. We validate the privacy model by showing that representative symmetric encryption protocols schemes from the literature naturally instantiate the proposed privacy classes.
In cloud computing environments, data sharing serves as a foundational enabler of collaborative operations across heterogeneous terminals. However, such sharing introduces critical challenges–including privacy leakage, inadequate data security, inflexible access control policies, and substantial computational latency. To address these limitations, this paper proposes a privacy-preserving, secure data-sharing framework tailored for edge-cloud collaborative architectures. Relative to conventional approaches, the proposed framework delivers three principal advancements: (1) User Privacy Protection: We design a secure query-matching algorithm that protects plaintext query keywords during data access. The Cloud Server (CS) performs matching over encrypted trapdoors without directly learning the queried keywords. (2) Computational Efficiency Improvement: Edge Servers (ESs) perform outsourced ciphertext transformation using user-specific transformation keys. The terminal only performs a lightweight local operation to recover the resource. This approach minimizes the computational overhead on the terminal side while safeguarding user privacy, and effectively reduces the overhead associated with user joining and revocation within the same group. (3) Fine-Grained, Policy-Driven Access Control: A cryptographically enforced, attribute- and keyword-aware access control mechanism is implemented, supporting precise, context-sensitive authorization decisions via encrypted keyword search and semantic matching–thereby enhancing both the security posture and operational flexibility of data access control.
Qikun Zhang, Zheng Cai, Jin-Bo Feng et al.· Journal of King Saud Univers...· 0 citations
Anonymous credentials (ACs) are a crucial cryptographic tool for privacy-preserving authentication in decentralized networks, allowing holders to prove eligibility without revealing their identity. However, a major limitation of standard ACs is the disclosure of the issuer’s identity, which can leak sensitive contextual information about the holder. Issuer-hiding ACs address this by making a credential’s origin indistinguishable among a set of issuers. Despite this advancement, existing solutions suffer from practical limitations that hinder their deployment in decentralized environments: inflexible credential models that restrict issuer and holder autonomy, flawed revocation mechanisms that compromise security, and weak attribute hiding that fails to meet data minimization principles. This paper introduces a new scheme called IRAC to overcome these challenges. We propose a flexible credential model that employs vector commitments with a padding strategy to unify credentials from heterogeneous issuers, enabling privacy-preserving authentication without enforcing a global static attribute set or verifier-defined policies. Furthermore, we design a secure decentralized revocation mechanism where holders prove non-revocation by demonstrating their credential’s hash lies within a gap in the issuer’s sorted revocation list while maintaining issuer anonymity. IRAC also strengthens attribute hiding by utilizing zk-SNARKs and vector commitments, allowing holders to prove statements about their attributes without disclosing the attributes themselves or the credential structure. Security analysis and performance evaluations demonstrate its practical feasibility for decentralized networks, where a credential presentation can be generated within 1 second.
Bin Xie, Rui Song, Xuyuan Cai et al.· IEEE Transactions on Network...· 0 citations
Cloud computing offers organizations scalable storage and computation, but outsourcing data processing to third-party infrastructure introduces serious privacy and confidentiality risks. Two complementary paradigms have emerged to address this challenge: homomorphic encryption (HE), which allows computation directly on encrypted data, and federated learning (FL), which enables collaborative model training without centralizing raw data. This paper presents a structured review of privacy-preserving data processing techniques for cloud environments built on HE and FL, individually and in hybrid combination. We propose a taxonomy of existing approaches, synthesize representative literature in a comparative table, illustrate a generic hybrid HE-FL architecture, and evaluate the two paradigms against criteria including data exposure, computational overhead, communication cost, resistance to inference attacks, and cloud deployment readiness. We further identify open challenges — including computational latency, key management, non-IID data distributions, and standardization gaps — and outline promising directions for future research, such as hardware-accelerated HE, adaptive encryption granularity, and standardized hybrid privacy frameworks for cloud-native machine learning.
Shivendra Shukla, Chandra Shekhar Gautam, Divyansh Tiwari· International Journal of Cre...· 0 citations
Private histogram computation is a fundamental building block for many data analytics tasks, enabling frequency analysis without revealing individual inputs. Existing protocols achieving robustness against malicious clients and servers typically require three servers with limited adversarial tolerance, restricting practicality.
In this work, we present Paras, the first two-server protocol for private histogram computation that achieves robustness against collusion between a malicious server and arbitrarily many malicious clients. Paras builds upon distributed point function-based approaches and introduces novel consistency checks leveraging vector oblivious linear evaluation (VOLE) to enforce both input correctness and output integrity. To realize these checks, we design two new cryptographic primitives: (1) aBV, an authenticated bit verification protocol that ensures VOLE committed shares correspond to valid bits, and (2) adIPA, an authenticated double inner product argument that enables secure consistency checks across two different VOLE sessions. These primitives may be of independent interest for other secure computation tasks.
We show that Paras is highly efficient and scalable: clients incur minimal cost independent of domain size, while servers achieve low per-client runtime, communication, and storage even at scale. For example, with 8192 clients over a domain of 128 inputs, each server requires only 14 ms runtime and 24 KB communication per client.
Dimitris Mouris, Lucas Piske, Pratik Sarkar et al.· IACR Communications in Crypt...· 0 citations
Retrieval-Augmented Generation (RAG) enhances large language models by incorporating external knowledge, but existing pipelines typically operate on plaintext data, raising significant privacy concerns. Prior work on privacy-preserving retrieval leverages cryptographic techniques such as homomorphic encryption (HE) and private information retrieval (PIR), but often relies on interactive protocols or ranking-based selection mechanisms that incur high latency and potential information leakage. In this paper, we propose a practical non-interactive encrypted retrieval framework for RAG based on threshold selection. Instead of performing expensive top-$k$ ranking under encryption, our approach selects documents whose similarity scores exceed a predefined threshold, reducing computational complexity from quadratic to linear in the corpus size. We implement this design using CKKS-based homomorphic computation, enabling fully encrypted similarity evaluation and document selection without revealing query content, intermediate scores, or selected indices. To bridge the gap between approximate encrypted computation and discrete token reconstruction, we introduce a precision-stable mask polarization method that ensures accurate recovery of selected documents. Experiments on standard retrieval benchmarks demonstrate that our approach achieves competitive retrieval effectiveness while significantly reducing latency compared to ranking-based encrypted methods. These results highlight threshold-based selection as a practical foundation for scalable and secure RAG systems.
Yang Gao, Gang Quan, Scott Piersall et al.· 0 citations
This work proposes PRoVeFL-a novel, modular FL framework that is Privacy-preserving, Byzantine-Robust, and ensures Verifiable aggregation, and improves runtime over the prior works, Prio and ELSA, based on distributed trust with comparable security guarantees, up to 100x and 10x, respectively.
Harsh Kasyap, Anil Kumar Pradhan, U. Atmaca et al.· 0 citations