The European Digital Identity Wallet has integrated anonymous credentials into its technical specifications, and singles out four constructions for privacy-preserving revocation, drawn from two families: positive dynamic accumulators and signed-pairs. The two families are described in the literature in substantially different terms, and no common basis for comparing them exists, which currently prevents informed and quantitative decision making. In this work, we give a unified treatment of...

Fully homomorphic encryption (FHE) lets a server run inference on encrypted data with strong privacy guarantees, but running a Transformer under FHE is expensive. Its non-linear operations, such as softmax, normalization, and activation, must be replaced with polynomial approximations that the CKKS scheme supports, and the depth of these approximations dominates inference cost. Existing FHE Transformers use hand-tuned approximation settings, such as iteration count and polynomial degree,...

Private Join and Compute (PJC) enables two parties to compute aggregates over matching records from their private datasets. In this work, we focus on the inner-product variant of PJC, which computes the inner product over matching records from their private datasets. It has important applications such as privacy-preserving ad conversion measurement. However, existing PJC protocols assume each party holds the entire dataset, which is often unrealistic in practice, where relevant datasets are...

With the arrival of scalable quantum computers, classical key exchange protocols like RSA, elliptic-curve and finite-field Diffie-Hellman are vulnerable to harvest-now-decrypt-later attacks. Quantum key distribution offers information-theoretic security but is sensitive to channel noise, loss, and distance, while post-quantum cryptography provides quantum resistance on conventional hardware at the cost of larger keys and a dependence on hardware computational strength. Existing hybrid...

Retrieval-augmented generation (RAG) services outsource vector search over proprietary corpora, yet clients cannot verify that returned context conforms to the promised index, parameters, and snapshot. We present VERIF, the first dedicated zero-knowledge polynomial interactive oracle proof (PIOP) for complete, service-consistent IVF-Flat retrieval. VERIF proves top-$m$ centroid selection, authenticated routing, exact full-vector scoring of every routed candidate, final top-$k$ selection, and...

MD-Hill-SPN is the first Hill-based construction to combine a multi-tier diffusion mix layer, a memory-hard KDF, and a simultaneous multi-metric empirical evaluation. Two independent runs of the full metric suite yield: (a) full plaintext avalanche from round 1 (mean 63.97–64.67 of 128 bits, ideal 64); (b) the differential-probability sampling floor of 2 × 10−5 reached at round 4 (50,000 of 50,000 output differences distinct, both sessions); (c) algebraic-degree lower-bound saturation...

Redeemable real-world asset tokens can trade onchain faster than their backing assets can be sold or settled. An immediate-exit facility cannot treat reported net asset value (NAV) as fully liquid. Within our scope, the mechanisms we compare do not jointly provide permissionless access, order-splitting resistance, and favorable small exits. In this paper, we propose a Proprietary Request for Quote (Prop RFQ), an onchain facility that prices exits against available reserves and mitigates,...

Zero-knowledge proofs are an operational infrastructure for rollups, private payments, verifiable execution, and zkVM applications. We treat them as succinct proofs of validity whose privacy properties depend on the system and the mode of use. Published benchmarks often compare different workloads, proof modes, and hardware paths, which makes their results hard to interpret. This work presents a unified host-agent framework for benchmarking heterogeneous proving systems under a common...

Decentralized lending protocols encode credit markets as smart contracts whose design choices affect gas cost, capital efficiency, liquidation behavior, and risk allocation. We compare Aave V3, SparkLend, Compound III, and Morpho Blue in matched Ethereum fork tests with wstETH collateral, USDC debt, and one fixed chain state. The tests measure call-path gas, annualized rates, thirty-day debt growth, maximum borrow capacity, and liquidation outcomes. It is important to note that the observed...

Decentralized Finance (DeFi) continues to experience rapid growth, yet a significant portion of capital remains inefficiently utilized in overprovisioned lending reserves or inactive liquidity positions. This paper presents two extension-based improvement proposals aimed at increasing capital efficiency in DeFi protocols. The first addresses idle capital in pool-based lending by adding an allocation layer that can deploy otherwise unused liquidity into external yield-generating strategies....

AI image generation has made image misinformation a serious concern. To address this issue, the Coalition for Content Provenance and Authenticity (C2PA) standard adopts digital signatures to attest that an image originates from an authorized source, such as an attested camera or authorized AI provider. In practice, however, raw images are rarely published directly: photos are typically compressed before publication, which invalidates any C2PA signature. Recent works have proposed the use of...

Sum-check and Fast Fourier Transforms (FFTs) dominate the computational cost of modern zero-knowledge proving systems, such as HyperPlonk (Eurocrypt 2023) and FRI-based schemes like DeepFold (USENIX Security 2025). Despite numerous optimizations, existing efforts remain fragmented across algorithmic, protocol, and implementation layers, leaving significant CPU performance potential untapped. We present MamaBearZKP, a co-designed framework that bridges these layers to enable...

Threshold signatures reduce the risk of single-key compromise by distributing signing authority, but each key share remains exposed to compromise of the software and infrastructure that execute the protocol. We present RSS, a threshold signing service that runs share generation and signing inside Trusted Execution Environments (TEEs). We integrate GG20 threshold ECDSA, FROST, and threshold BLS into the EnGINE experimentation framework and evaluate local and Google Cloud deployments using...

Blockchain networks rely on transaction fees for resource allocation and spam prevention. Ethereum's gas mechanism and its adoption by Layer-2 rollups serve this dual purpose, but gas-based fee markets produce unintended consequences: ineffective spam deterrence at low fee levels, poor user experience, privacy leakage, and revenue instability for rollup operators. We present an idealized protocol architecture for gasless sequencer admission in Ethereum Layer-2 rollups based on Rate-Limiting...

Privacy-preserving payment systems are well understood, yet their adoption in regulated settings, such as central bank digital currencies (CBDCs), institutional stablecoins, and other compliant payment infrastructures, has been limited by concerns over their potential misuse for illicit activities. Regulators counter financial crime with a toolbox of complementary measures to identify, trace, and stop criminal actors. Tracing is one key tool: acting on outside evidence that a user is...

Recent works have highlighted the risk of deterministic memory encryption, as used for example in AMD SEV. In the so-called ciphertext side-channel attack, an adversary leaks sensitive information, such as cryptographic keys, from an encrypted VM by observing changes in the encrypted contents of the memory. To date, countermeasures have been quite restricted in scope and have not addressed the root cause. Alternatively, some studies suggest adding freshness to memory encryption. However,...

Lattice-based cryptography offers a promising direction for transitioning Bitcoin toward post-quantum security, serving as a secure replacement for currently deployed discrete logarithm signatures. The primary advantages of lattice-based signature schemes include the compact combined size of signatures and public keys (e.g., in some cases below 1.6 KB), the robustness of underlying security assumptions, and an algebraic structure that, while not yet yielding practical constructions, holds...

Distributed Oblivious RAM (DORAM) is a promising building block for privacy-preserving cloud databases and outsourced storage systems. However, existing two-server designs often rely on slow linear scans or heavy cryptographic primitives, making them struggle to balance efficiency and bandwidth, and thus hindering their practical deployment. We present DuetORAM, a two-server DORAM that achieves constant-round access with $O(\log N)$ communication while avoiding these computational...

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...

Video provenance is an important problem on the modern internet. In response, the Coalition for Content Provenance and Authenticity (C2PA) has developed a standard for verifying video and image provenance where cameras sign captured videos with an on-device secret key. Since videos are generally edited and resized before be- ing posted, the C2PA signature from a camera cannot be used as is to verify provenance of published videos. Prior work has developed zero-knowledge techniques for...

Payment Channel Hub (PCH) improves blockchain scalability by enabling off-chain transactions via an untrusted intermediary known as the tumbler. However, existing PCHs either fail to guarantee the unlinkability privacy or rely on inefficient non-interactive zero-knowledge (NIZK) proofs. Recently, Ge et al. proposed Accio, a privacy-preserving PCH that eliminates the need for NIZK proofs. Nevertheless, Accio only supports unidirectional channels which results in high on-chain costs and...

We study auctions where two positions are sold to unit-demand bidders with private heterogeneous order preferences: some are specialists who value only the first position, while others are generalists indifferent between the two. First, we consider a first-price rule which allocates the first and second items to the highest and second-highest bidders, respectively. We show that no strategy profile ex-post implements the efficient allocation at every type profile, irrespective of payments,...

Large language model (LLM) is increasingly deployed as a remote service, where users rely on third-party servers to perform computation. However, such settings introduce critical integrity concerns, as an untrusted server may deviate from the prescribed computation, skip expensive operations, or return incorrect results, while users lack practical approaches to verify execution correctness. Ensuring the correctness of LLM inference under untrusted execution remains a fundamental challenge....

Trust-minimized bridges on Bitcoin move SNARK verification off chain by evaluating the verifier as a garbled circuit. The bridge's on-chain spending condition obliges the Garbler to reveal the labels for one input without enabling the Evaluator to derive labels for any other input. Existing designs commit to each input bit with a Lamport signature which is costlier on chain, or with adaptors where there is no guarantee that the spend actually reveals the labels. We present Antichain...

Online attribute checking (e.g. proving age, residency) is increasingly common, yet standard implementations reveal far more personal information than necessary (e.g. all ID contents). Privacy-preserving alternatives exist but require digital inputs: anonymous-credentials or zero-knowledge (ZK) proofs of signature possession over a bitstring. However, it is challenging to gain integrity guarantees on the bitstring itself. C2PA offers a partial solution: C2PA-enabled cameras...

Recently, zero-knowledge proof protocols have gained much popularity due to the adoption in blockchain applications, e.g., zero-knowledge virtual machines. However, using the current standardized hash functions inside the generation of zero-knowledge proofs would incur much overhead in proof size, as well as prover and verifier’s runtime. In the past few years, various circuit-friendly hash functions has been proposed. Skyscraper-v2 is one example of such hash functions applying the...

Privacy-preserving machine learning under fully homomorphic encryption (FHE) faces a structural limitation: numerical precision is bound to cryptographic parameters and key material, forcing precision to be fixed at scheme initialization. Existing frameworks must regenerate keys or recompile circuits whenever bit-width changes, eliminating precision as a deployment-time performance knob and making mixed-precision strategies - widely used in plaintext machine learning - impractical under...

PRIVIDEMA project (Privacy-Preserving Identity Management for Digital Wallets and Secure Data Sharing and Processing for Cyber Threat Intelligence Data) advances the state of the art in cryptographic and Privacy-Enhancing Technologies (PETs) to enable secure, interoperable, and trustworthy data exchange across sectors, with a focus on the domains of Cyber Threat Intelligence and Digital Identity Management. This paper presents two representative real-world use-cases: (1)...

Internet shutdowns are used authoritarian regimes to suppress communication that end up crippling essential Internet-driven services, besides the obvious silencing of dissent. Traditional tools like VPNs and Tor, dependent on active Internet connections, falter during these blackouts. Earlier solutions, such as Dolphin, delivered meagre bandwidth and weak privacy safeguards, exposing a glaring weakness in the battle against digital oppression. ViNET, a system that cleverly repurposes...

This work presents ConvertInput-Free Vector Homomorphic Secret Sharing (Vector-HSS), a novel HSS primitive based on the Decisional Composite Residuosity (DCR) assumption. Our construction enables efficient high-dimensional vector computations while avoiding the costly $\texttt{ConvertInput}$ operation. As a unified framework, Vector-HSS can be used as a building block for Private Information Retrieval (PIR), Secure Multi-Party Computation (MPC), Privacy-Preserving Machine Learning (PPML),...

Vector commitment (VC) schemes enable a prover to commit to a vector and later open any position with a short proof. However, existing VC schemes are designed for centralized settings, and cannot work in decentralized systems, where the input vector is distributed across multiple machines. Similarly, traditional VC schemes cannot leverage distributed parallel computation across multiple machines for acceleration. To tackle this issue, we introduce a new notion—distributed VC (DVC), which...

The Signal Protocol’s Double Ratchet and X3DH/PQXDH handshakes give in-transit messages forward secrecy and post-compromise security: compromising a session key does not expose past traffic, and the protocol self-heals after a fresh Diffie–Hellman step. Encrypted backups, by contrast, are commonly protected by a single static secret, a “Backup Recovery Key” generated once and held constant until manually rotated. We show, with an explicit attack, that this baseline design provably fails even...

We observe that the existing norms within cryptography do not expect protocol analysts to document the sociotechnical properties that a deployed system should have. To help close this potential gap, we develop a framework that allows bringing sociotechnical dimensions into analyses of cryptographic systems, and in particular facilitates "in-context" analysis of proposed cryptographic deployments on top of widely-accepted cryptographic modeling techniques. To explore the utility of our...

Recent advances in fully homomorphic encryption (FHE) have enabled privacy-preserving machine learning directly over encrypted data. As a representative recurrent architecture, the long short-term memory (LSTM) network is widely used for modeling sequential dependencies, yet existing FHE-based LSTM inference schemes still suffer from high latency and limited scalability. In this paper, we present an efficient privacy-preserving LSTM inference protocol on encrypted sequential data based on...

Free Hosting Platforms (FHPs) let users publish websites with minimal cost and configuration, but the same provider-managed infrastructure can also be used to host phishing websites. We study how this setting affects Certificate Transparency (CT)-based phishing detection by analyzing X.509, CT, and URL features across FHP phishing, FHP benign, non-FHP phishing, and popular benign websites. Reflecting the shared and wildcard certificate practices common in this setting, we analyze the...

In the information-theoretic model, parties communicate over sender-authenticated point-to-point channels, but use no digital signatures or other transferable cryptographic certificates; the adversary is otherwise computationally unbounded. We present \name, an information-theoretic Byzantine agreement protocol for partial synchrony with a good-case latency of two rounds that achieves the optimal resilience bound of $n = 5f - 1$ in this setting. When the actual network delay after GST is at...

Proof-of-useful-work (PoUW) certifies computational hardness, not utility: a certified computation need not be anyone’s demanded job. We separate three properties of a work receipt—work soundness \(\mathsf{W}\), job binding \(\mathsf{B}\), and demand exogeneity \(\mathsf{E}\)—and locate the gap at \(\mathsf{E}\). Two results require no equilibrium or market model. First, payments between coalition-controlled requesters and workers are recoverable transfers that contribute no Sybil-resistant...

As the usage of Artificial Intelligence (AI) for sensitive purposes increases, there is a growing need for privacy-aware explainable AI (XAI) tools. In this paper, we present a privacy-preserving counterfactual explanation algorithm. Our starting point is a decision-support model that is able to operate on vertically partitioned datasets, meaning that each party holds a different subset of datapoint attributes. The goal of a counterfactual algorithm is to find, given an observation, a...

Post-quantum cryptography (PQC) standardisation reached a pivotal milestone in August 2024 with the release of NIST FIPS 203 (ML-KEM) and FIPS 204 (ML-DSA), yet the vast majority of deployed public-key infrastructure continues to rely on RSA-2048 and Elliptic Curve Diffie-Hellman (ECDH), both vulnerable to Shor's algorithm on a cryptographically relevant quantum computer. The Harvest Now, Decrypt Later (HNDL) threat renders this risk operationally present: adversaries may archive ciphertext...

Classical deep learning for differential cryptanalysis requires millions of ciphertext pairs, rendering attacks infeasible or easily detectable. This work overcomes this data limitation by introducing quantum differential distinguishers, enabling a practical attacker model where executing few queries is feasible. We design these distinguishers via quantum machine learning based on variational quantum circuits. To address circuit design challenges, we propose QuantumScouter, a reinforcement...

Retrieval-augmented generation (RAG) grounds large language models on external knowledge by using vector similarity search over document embeddings to identify relevant documents and supplying their contents as generation context. However, its retrieval stage creates privacy risks: user queries may reveal sensitive intents, while the database may contain proprietary or confidential records. Existing secret-sharing-based solutions that protect both query and database privacy remain costly. To...

The California Consumer Privacy Act (CCPA) grants consumers the right to request deletion of personal data held by registered data brokers. California’s 2023 Delete Act provides a new mechanism through which consumers can exercise this right by leveraging a centralized Delete Request and Opt-out Platform (DROP), enabling users to submit a single request that must be periodically processed by all registered data brokers. Since this platform aggregates sensitive user information, it is...

As privacy concerns rise, numerous laws require machine learning-based applications to comply with stringent privacy regulations. While Homomorphic Encryption (HE) allows computation directly on encrypted data, existing HE-based inference solutions suffer from significant computational and memory overhead for both single and multiple samples. Additionally, current methods require many rotation keys, which limits their practicality in a broader range of scenarios. To address these...

With growing concerns over data privacy, private nearest neighbors search (PNNS) attracts increasing research attention. Existing PNNS follow two main approaches: i) interactive PNNS based on secure multi-party computation protocols that leverage index structures to achieve sublinear complexity, and ii) non-interactive PNNS utilizing fully homomorphic encryption to minimize communication bandwid that the cost of superlinear computational complexity. To address the communication-computation...

In next-generation networks, communication systems will no longer be limited to data transmission and will be expected to acquire awareness of the surrounding environment. This leads to the concept of integrated sensing and communication (ISAC), where the same wireless infrastructure is used for both communication and environmental sensing. Thus, ISAC enables the system to transmit information efficiently and observe and interpret channel variations and user behavior. Motivated by this...

Modern hardware security heavily relies on the assumption that pre-synthesis algorithmic protections will survive the physical fabrication pipeline. However, untrusted third-party Electronic Design Automation (EDA) toolchains often apply aggressive structural optimizations that can silently compromise perfectly symmetric designs, introducing critical data-dependent power side-channel vulnerabilities. Existing pre-silicon verification methodologies require exposing highly sensitive,...

In this work we propose a novel Boolean lookup-table evaluation methodology over binary CKKS when circuit XORs are kept lazy, i.e., evaluated as additions whose least significant bits remain correct. Our method represents a LUT in the Walsh basis, forms the required parity sums by lazy CKKS additions, and packs them into ciphertext slots. We then use CKKS binary bootstrapping as a refresh step: the StC stage maps the packed lazy parities to MSB-encoded bits, removing the overflow; CtS places...

Privacy-Preserving Machine Learning (PPML) is a methodology designed to maintain data privacy throughout the machine learning pipeline. Although cryptographically protecting input privacy at the training phase is theoretically feasible, it remains computationally intensive in practice. As such, most recent works in this area focus on the inference phase. In contrast, we consider the training phase. Our goal is to enable machine learning engineers to use customer data earlier in the ML...

Blind watermarking enables image ownership verification without requiring the original image. However, existing schemes usually require the owner to reveal the watermark seed and watermark positions during extraction. This creates a strong trust assumption on the verifier. Once such secrets are exposed, a malicious verifier can forge or remove the watermark. In this paper, we present TIM, the first publicly verifiable blind watermarking scheme with sensitive parameters privacy based on...

Secure/Multipurpose Internet Mail Extensions (S/MIME) is a standards-based mechanism for certificate-backed email signing and encryption. Its post-quantum migration is now technically actionable: ML-KEM public keys can be represented in X.509 certificates, and CMS can carry ML-KEM recipient information through \texttt{KEMRecipientInfo}. These standards solve an encoding problem, but they do not by themselves solve an assurance problem. A mailbox may possess a post-quantum-capable certificate...

Semantic search over vector databases is a fundamental problem in both theory and practice, with large-scale systems relying on approximate nearest-neighbor (ANN) algorithms to retrieve semantically similar results efficiently. Achieving this capability securely while keeping both data and queries hidden remains a major challenge. Existing secure semantic search systems incur high latency and fail to scale to realistic database sizes. We present $\mathsf{BiSON}$, the first secure...

This paper presents RiskService, a modular risk assessment module integrated into the Q-OPSEC adaptive AI middleware for quantum cryptography. A synthetic dataset covering 58 features across nine groups, including behavioral, device, network, authentication, and LLM-derived signals, feeds a training pipeline evaluating six model families under class-imbalanced conditions. LightGBM achieves the best performance, with AUC-ROC of 0.9895, average precision of 0.9344, and Brier score of 0.0421 at...

Post-quantum signature algorithms pose significant challenges for DNSSEC migration: their larger keys and signatures exceed DNS over UDP transport limits, making TCP fallback unavoidable even for the most compact schemes. We propose a differentiated algorithm selection, assigning distinct signature algorithms to the Zone Signing Key (ZSK) and Key Signing Key (KSK) roles. This approach expands the space of deployable post-quantum configurations beyond what undifferentiated selection permits,...

Secure Multi-Party Computation (MPC) enables collaborative analytics without exposing raw data. Yet, complex data retrieval operations in relational databases (i.e., relational queries) remain limited by scalability bottlenecks: oblivious operators must pad intermediate results to worst-case sizes to prevent information leakage, thereby inflating communication and computation costs. We propose Resizer, a lightweight operator that can be inserted transparently into relational query plans to...

In 2020, a coordinated law-enforcement effort infiltrated Encrochat, an end-to-end encrypted service provider, exfiltrating historical and real-time data and metadata over months. Encrochat was used extensively by organised crime, and the data from the operation was used as supporting evidence in over 6,000 arrests and related prosecutions across Europe. Encrochat's architecture was vertically integrated, with the company acting as both a device vendor and service provider; Encrochat sold...

As synthetic speech becomes increasingly realistic, the need to authenticate audio recordings has become more urgent. Such authentication should allow a released recording to demonstrate that it was captured by an authorized device and modified only through the declared edits, while concealing both the unreleased content and the identity of the particular recorder. In this work, we propose PPAAS, a privacy-preserving framework for authenticated audio editing under a formal security model....

Secure computation allows multiple parties to jointly evaluate a function without leaking their individual inputs. An intrinsic issue with these techniques is that they do not offer any protection against parties which may contribute bad quality or even maliciously crafted data. We introduce TRIP, a protocol which protects against malicious manipulations of the input in secure computation of linear regression tasks. Linear regression is the cornerstone in many machine learning tasks,...

Transformer models have recently emerged as a game-changing technology for applications in content generation. However, severe privacy concerns and the scalability bottlenecks of existing secure protocols limit their large-scale deployment. To address these challenges, we present CRAfT, an innovative system designed for high-scalability and low-latency secure Transformer inference. The core contributions of CRAfT are threefold: First, we propose a novel ciphertext packing strategy tailored...

Fully homomorphic encryption (FHE) enables privacy-preserving neural network inference but suffers from high overhead from homomorphic convolutions, polynomial activation approximations, and CKKS bootstrapping. This paper presents BootNet, a unified framework that fuses all three operations into a single bootstrapping invocation per CNN layer, achieving convolution, ReLU, and noise refresh simultaneously. Prior works are able to fuse convolution into bootstrapping using CinS encoding...

Modern, deployed key transparency systems rely on auditors to ensure that updates to the set of keys are well-structured, allowing clients to efficiently monitor their own keys. In practice, the server's consistency proofs are very large, requiring computationally powerful auditors; as a result, real-world deployments have very few auditors. We propose a new key transparency system based on a new data structure called Forget-me-not trees, which is a careful composition of Merkle trees...

As the transition to post-quantum cryptography accelerates, security protocols must evolve to resist quantum threats while remaining practical, particularly on constrained devices where memory, bandwidth, and performance are limited. We consider the NIST Personal Identity Verification (PIV) system, where smart cards rely on digital signatures for authentication. Since post-quantum signatures introduce substantial computational and memory overhead, whereas post-quantum Key Encapsulation...

We improve the performance of the lattice-based cryptosystem Dilithium on AVX2 and NEON by deeply exploiting its algorithmic properties, such as small coefficient bounds and high sparsity, with the distinct instruction-level profiles of the underlying architectures. On AVX2, we deploy a single-modulus 16-bit NTT for $c \cdot \mathbf{s}_i$ and a multi-moduli 16-bit NTT coupled with a vectorized CRT reconstruction for $c \cdot \mathbf{t}_0$. These instruction-level optimizations accelerate the...

As machine-learning-as-a-service (MLaaS) becomes ubiquitous, protecting model queries via private inference is increasingly critical. Existing homomorphic encryption (HE)-based protocols for Private Decision Tree Evaluation (PDTE) have server complexity that scales at least as $O(2^D)$ in the tree depth $D$, so the cost of evaluating each tree grows exponentially with depth; in gradient boosted decision tree (GBDT) ensembles, where predictions aggregate the outputs of many trees, this...

Satellite communication systems, as critical long-lifecycle infrastructure, face a dual security challenge in the coming decades: the threat of quantum computers and the operational rigidity of traditional Public Key Infrastructure (PKI). While migrating to Post-Quantum Cryptography (PQC) addresses the former, it fails to solve the inefficiency of certificate management, where in-orbit policy updates require a prohibitively slow and complex multi-party re-issuance process. To address...

Digital library lending is a critical resource for access to information. Currently prevalent models of digital lending, however, involve opaque licensing schemes that entail serious drawbacks to reader privacy and freedom of expression. In popular modern library apps, publishers and hidden intermediaries control a wealth of informa- tion about readers and reading habits, at a scale and level of detail that would be essentially impossible in physical library lending. To understand...

We introduce the Huffman-Merkle Tree (HMT), an authenticated data structure (ADS) optimized for dynamic workloads where some items may be more frequently accessed than others, and access frequencies change over time. An ADS allows proving item membership against a short commitment to a large mutable state, with applications including verifiable storage, Internet transparency services, and blockchains. Optimizing ADS performance under continuously changing access frequencies has not been...

Modern consensus protocols often aspire to be responsive---that is, to confirm transactions in time proportional to the actual network delays as opposed to a (typically much larger) worst-case bound on network delays. Responsiveness can yield substantial practical improvements in both protocol latency and throughput. In blockchain settings, however, block proposers commonly have economic incentives (most notably MEV) to delay their blocks, a phenomenon repeatedly observed in practice,...

All EU member states are required to roll out a digital identity system - the European Digital Identity (EUDI) wallet - by the end of 2026. Strong privacy is at the core of the underlying regulation, which mandates the EUDI wallet to support selective disclosure and unlinkability. The wallet currently being developed relies on the batch issuance of one-time ECDSA credentials that sign attributes through individually salted hashes for selective disclosure. This solution is known to achieve...

Federated fine-tuning of Large Language Models via Low Rank Adaptation (LoRA) faces a critical privacy-efficiency trade-off: low-rank factors can leak sensitive data, yet standard secure aggregation is restricted to linear operations. Existing solutions for aggregating matrix products (e.g., $\mathbf{B}_i \mathbf{A}_i$) either sacrifice exactness, depend on a trusted third party, or incur prohibitive costs at scale. We present SecLoRA, the first decentralized framework achieving exact...

Overbroad computer crime, intellectual property, and other laws are well known to create legal risks that can discourage essential research. Notable examples include the US Computer Fraud and Abuse Act and the UK Computer Misuse Act. Because such laws fail to distinguish malicious hacking from good-faith testing and research, researchers face serious legal risks for public-interest research activity like identifying software or hardware vulnerabilities or scraping data. Despite the research...

Functional bootstrapping has made discrete computation practical in the Cheon-Kim-Kim-Song (CKKS) scheme, but it fuses four distinct tasks - lookup table (LUT) evaluation, modular reduction, noise cleaning, and ciphertext refreshing - into a single rigid pipeline. As a consequence, a generic LUT over an alphabet of size $t$ costs multiplicative depth proportional to $\log_2 t$ and consumes a large share of the modulus budget during a fixed bootstrapping procedure, invoked each time a LUT...

While FHE enables computation on encrypted data, protecting the program itself remains a theoretical and practical challenge, often forcing practitioners to choose between exposing proprietary logic or suffering impractical performance penalties. This paper introduces HEGIDE, an oblivious processor based on the (discrete) Cheon-Kim-Kim-Song (CKKS) scheme that bridges the gap between theoretical Private Function Evaluation (PFE) and its practical realization. Central to our contribution is...

The Korean Cryptographic Module Validation Program (KCMVP) is a national certification system that verifies the security and conformity of cryptographic modules deployed in government and public institutions. The current process typically takes about one and a half years, during which frequent supplement requests and the resulting retesting cycles substantially raise costs and delay schedules. To address this, we propose an AI-based pre-certification framework that combines rule-based...

The advancement of quantum computing threatens public-key cryptographic algorithms used in TLS connections, such as RSA and ECDHE. The Harvest Now, Decrypt Later (HNDL) attack exposes long-term confidential data to risk even before quantum computers are practically realized, yet a systematic methodology for immediately quantifying the quantum vulnerability of individual TLS connections remains absent. This paper proposes a layered risk scoring model that quantifies the quantum threat...

The “Harvest Now, Decrypt Later (HNDL)” attack—which intercepts and stores encrypted communications before quantum computers become practical and decrypts them afterward—poses a long-term threat to TLS communication channels. Following the completion of NIST PQC standardization, organizations must pursue a phased migration of their TLS layers; however, existing approaches are limited by late detection of configuration errors, absence of cryptographic asset tracking, and insufficient...

Cryptocurrency lending is growing rapidly, and smart-contract-based loans are expected to grow further. However, existing systems are fundamentally limited: they only operate on smart-contract-enabled blockchains, and assets from other blockchains can be used only via tokenized representations. In this work, we propose an oracle-aided cryptographic protocol that implements the logic of collateralized loans without smart contracts, instead only requiring basic transactions from the...

Public permissionless ledgers are append-only by design, yet are increasingly asked to host data subject to statutory erasure obligations (GDPR Art. 17, EDPB Guidelines 02/2025). The prevailing cryptographic response, rewriting the chain via chameleon hashes and redactable-blockchain constructions, is structurally unavailable on Bitcoin and Ethereum mainnet. This SoK systematizes the orthogonal application-layer design space, in which the ledger is left untouched and erasure is achieved by...

Techniques to randomly select sets of anonymous parties are ubiquitous in efficient and adaptively secure consensus protocols, as well as in Multi-Party Computation in the YOSO model, where each round is executed by a different random anonymous committee. Anonymous committee selection aims at randomly selecting a set of $n$ parties (the committee), where at most $t$ parties are corrupted (except with negligible probability), drawing from a population of $N \gg n$ parties with at most $T$...

Demand Response (DR) in energy systems is a flexibility mechanism enabling consumers to modify their electricity demand in response to signals from network operators, designed to ensure power grid reliability. In particular, incentive-based DR programs, in which consumers provide load reduction in exchange for financial remuneration, have proven more effective than alternative approaches such as price-based programs. However, incentive-based approaches have taken only partial account of...

While deep learning is ubiquitous, centralized pro- cessing exposes sensitive sequential data—such as natural lan- guage—to untrusted servers, forcing an unacceptable privacy- utility trade-off. Fully Homomorphic Encryption (FHE) re- solves this by computing directly on encrypted data. However, standard neural networks ported to FHE suffer from severe latency bottlenecks, particularly because continuous non-linear activations dominate the computational budget. To overcome this, we...

Decentralized exchanges built on Automated Market Maker (AMM) protocols have become a cornerstone of Decentralized Finance (DeFi), offering token swaps without conventional order-book matching. However, supplying liquidity to these AMM pools exposes participants to distinctive market risks, most notably impermanent loss (IL) - the potential underperformance of a liquidity-provider portfolio relative to simply holding the underlying tokens. This paper presents a comprehensive overview of IL,...

Integrated sensing and communication (ISAC) is a promising feature of future communication networks. While spatial sensing can improve network performance and enable external services, it also creates privacy challenges that go beyond the confidentiality of communication content. Future networks using millimeter-wave (mmWave) and sub-terahertz (THz) frequencies may collect or infer detailed information about people, devices, bystanders, passive objects, and environments in a sixth-generation...

Zero-Knowledge (ZK) scaling solutions have seen wide adoption recently in emerging technologies, such as cryptocurrencies. Yet, the concrete limits of current ZK proof systems are not well understood for an emerging class of particularly compute-heavy operations -- artificial intelligence algorithms. To that end, this technical whitepaper explores the current limits of constructing proofs for machine learning computation. We do this by benchmarking a common suite of multi-layer...

This systematic literature review investigates recent advancements in machine learning techniques aimed at ensuring safety and security in autonomous systems. By analyzing 129 scholarly articles published between 2018 and 2024, we identify dominant methodologies, prominent machine learning techniques, and key application domains. As the paper illustrates, Reinforcement Learning (RL) and Deep Learning (DL) have emerged as leading approaches, especially in contexts requiring real-time...

This literature review explores the state-of-the-art advancements in quantum and post-quantum blockchain. The realm of quantum computing is on the rise and will disrupt entire tech industries, including classical cryptography, which is the foundation of blockchain. There has been extensive research on classical cryptosystems (i.e., post-quantum) and their integration with blockchain to create quantum-resistant classical blockchains. We have reviewed the state-of-the-art in these post-quantum...

We present a method for secure pattern matching over encrypted texts using TFHE. Our approach realizes a fully secure binary search algorithm by leveraging two operational modes of integer-input TFHE. While the BGV-based method of Bonte and Iliashenko (CCSW '20) requires $O(|P| \cdot |T|)$ secure character comparisons to find a pattern $P$ in a text $T$, our method reduces this to $O(|P|\log |T|)$ comparisons, achieving improved scalability for large texts. As a result, our method can...

Active weighted threshold signatures support dynamic changes to signer weights, thresholds, and committee membership. We show that local validity of weighted update operations is not a compositional security abstraction: a sequence of individually valid updates can move an initially sub-threshold coalition into an authorized reachable state. We introduce rank-exposure guards, a compiler that enforces a reconstruction-safety invariant over live, stale, derivative, public, and transient...

End-to-end encrypted (E2EE) messengers such as Signal, WhatsApp and iMessage increasingly deploy Key Transparency (KT) to make malicious key substitution detectable. Yet KT only delivers its intended protection if users are anchored to the same global append-only KT history. A malicious operator can break this condition by equivocating, presenting incompatible views of the KT directory to different clients. Current deployments delegate detection to a small set of third-party auditors,...

Modern portfolio theory (MPT) prescribes how to maximise the return of an asset portfolio for a given level of risk. The optimal trade-off between return and variance defines the efficient frontier. Whether actual cryptoasset portfolios approximate this prescription and whether proximity to the frontier translates into realised performance remain difficult to test at large scale in traditional markets due to their opaque nature and the inaccessibility of data. As we show, public blockchains...

The transition to post-quantum cryptography (PQC) digital signatures poses an unexpected threat to the storage structure of relational databases. At the same security level, the AIMer-192f signature reaches 13,056\,B, which is more than 13 times that of RSA-7680 (960\,B). Storing it inline in MySQL InnoDB causes the B$^+$-Tree fan-out to collapse from the theoretically predicted value of 167 to a measured value of 1. This result experimentally reveals that the off-page storage model in the...

We define a protocol by which we can recognize if a person is the owner of an object. The object can, for instance, be an official document such as a diploma. In our model, the object has an attached RFID chip. The owner is enrolled when the document is created and the chip is attached. Later on, public verifying terminals can verify if a person is the enrolled owner by means of biometric recognition. Hence, the terminal must scan both the chip and the person. As an implementation...

Cloud computing has become the standard for large-scale computation, offering elastic scalability and on-demand resources that exceed typical on-premise capabilities. However, many large-scale computations over sensitive data -- such as genome-wide association studies (GWAS) -- face significant barriers to cloud adoption due to privacy concerns and regulatory constraints. While cryptographic primitives like multi-party computation can alleviate these concerns through provable privacy...

Digital identity systems are being deployed worldwide to enable secure, privacy-preserving authentication. In this regards, Europe's EUDI Wallet mandates selective attribute disclosure and unlinkable authentication, effectively requiring anonymous credentials. Yet current proposed solutions omit them because they lack device binding: tying a credential to a secure hardware element on the user's device to prevent cloning or sharing. Device binding encodes a device public key in the credential...

To address the expanding attack surface caused by increasing digitization and interconnection, operators of Industrial Automation and Control Systems (IACS) adopt security measures already established in information technology, such as Public Key Infrastructure (PKI), to Operational Technology (OT). However, operating a PKI proves to be challenging in complex and heterogeneous IACS landscapes. Hence, operators might rely on external PKI service providers, resulting in new trust dependencies...

Proof of Stake (PoS) blockchain systems require weighted threshold signatures where participants’ voting powers reflect their stakes. As stakes change dynamically through deposits and withdrawals, efficient weight and threshold adjustments are essential for maintaining system security and availability without downtime. However, existing approaches face critical limitations: (1) virtualization-based schemes require $O(w)$ operations (signatures) per participant with weight w; (2) dynamic...

The EU Whistleblower Directive aims to create a framework where the persons reporting breaches of EU law are protected against retaliation. In contrast to GDPR, it is mainly based on trust assumptions and not on the concept of privacy and security by design. As we are explicitly dealing with problems of unlawful behavior, this is a critical issue. In this paper, we analyze the role of pseudonymization, the main technical tool promoted in the GDPR, within the Whistleblower Directive. To see...

Training machine learning models is computationally intensive, making cloud-based outsourcing an attractive solution to alleviate local resource constraints. However, untrusted cloud environments pose serious privacy risks to both training data and resulting models. Existing works primarily rely on multi-party computation (MPC) or lattice-based Homomorphic Encryption (HE), which often incur high communication or computation overheads. To address these challenges, we propose an efficient...

Although recent surveys on privacy-enhancing technologies concluded that FHE cannot feasibly evaluate non-linear activation functions in modern ML architectures, 20 CKKS-based frameworks have since demonstrated end-to-end private inference of LLMs with up to 8B parameters. However, as the field grows rapidly, the literature has become fragmented. Frameworks differ in ciphertext packing layouts, model fidelity, software and hardware stacks, and reported metrics, which hinder direct comparison...

Bitcoin bridges, protocols that lock BTC on Bitcoin and represent it on a secondary system, underpin much of Bitcoin's application layer, yet remain poorly secured. Deployed bridges rely on federated custody with honest-majority assumptions, while BitVM2, the state of the art in trust-minimized bridging, incurs worst-case dispute costs of approximately \$16,000, requiring large operator bonds and deposits that restrict participation to well-capitalized parties. We present BitVM3-BRIDGE, a...

Privacy-preserving machine learning (PPML) enables inference over sensitive data without exposing raw inputs, with CKKS being a widely adopted scheme for approximate arithmetic. However, existing CKKS implementations are primarily based on 64-bit residue number system (RNS) representations, creating a mismatch with modern GPUs optimized for 32-bit integer arithmetic. This mismatch introduces substantial computational overhead, limiting the practicality of encrypted transformer...