Reviewerly/exHarmony huggingface dataset:
Dealing with Over-Reliance on Background Graph for Few-Shot Knowledge Graph Completion

Few-shot knowledge graph completion (FKGC) has drawn growing research attention recently, aiming at inferring new triples using only a small number of related references. Most existing FKGC methods encode few-shot relations by capturing signals from local neighbors of each entity. However, this process dramatically relies on a thorough background knowledge graph (background KG) that is space-consuming to storage and often inaccessible in real-life cases. Moreover, they tend to overlook the underlying correlational information coming with different relations. In this paper, we attempt to address FKGC task in a practical scenario where background KG is not provided and propose a novel framework called ICOM (I Can Count On Myself) which aims at making full use of few-shot instances within and across relations. Specifically, we go deep into the interaction between entity pairs within specific relation and devise a 3D convolutional meta-relation learner to extract relation-specific features straightly from triple. To accumulate knowledge across relations, an analogical enhancer is then built to leverage the semantic relational correlations attentively so that correlative relations can complement each other on representation learning for better link prediction. Finally, we introduce meta-learning technique for faster adaption. Empirical studies on three real-world FKGC datasets demonstrate that ICOM shows superiority over competitive baseline methods and achieves new state-of-the-art results with different few-shot sizes.
Bingo: Adaptivity and Asynchrony in Verifiable Secret Sharing and Distributed Key Generation

We present $$\textsf{Bingo}$$ , an adaptively secure and optimally resilient packed asynchronous verifiable secret sharing (PAVSS) protocol that allows a dealer to share $$f+1$$ secrets with a total communication complexity of $$O(\lambda n^2)$$ words, where $$\lambda $$ is the security parameter and n is the number of parties. Using $$\textsf{Bingo}$$ , we obtain an adaptively secure validated asynchronous Byzantine agreement (VABA) protocol that uses $$O(\lambda n^3)$$ expected words and constant expected time, which we in turn use to construct an adaptively secure high-threshold asynchronous distributed key generation (ADKG) protocol that uses $$O(\lambda n^3)$$ expected words and constant expected time. To the best of our knowledge, our ADKG is the first to allow for an adaptive adversary while matching the asymptotic complexity of the best known static ADKGs.
List Oblivious Transfer and Applications to Round-Optimal Black-Box Multiparty Coin Tossing

In this work we study the problem of minimizing the round complexity for securely evaluating multiparty functionalities while making black-box use of polynomial time assumptions. In Eurocrypt 2016, Garg et al. showed that assuming all parties have access to a broadcast channel, then at least four rounds of communication are required to securely realize non-trivial functionalities in the plain model. A sequence of works follow-up the result of Garg et al. matching this lower bound under a variety of assumptions. Unfortunately, none of these works make black-box use of the underlying cryptographic primitives. In Crypto 2021, Ishai, Khurana, Sahai, and Srinivasan came closer to matching the four-round lower bound, obtaining a five-round protocol that makes black-box use of oblivious transfer and PKE with pseudorandom public keys. In this work, we show how to realize any input-less functionality (e.g., coin-tossing, generation of key-pairs, and so on) in four rounds while making black-box use of two-round oblivious transfer. As an additional result, we construct the first four-round MPC protocol for generic functionalities that makes black-box use of the underlying primitives, achieving security against non-aborting adversaries. Our protocols are based on a new primitive called list two-party computation. This primitive offers relaxed security compared to the standard notion of secure two-party computation. Despite this relaxation, we argue that this tool suffices for our applications. List two-party computation is of independent interest, as we argue it can also be used for the generation of setups, like oblivious transfer correlated randomness, in three rounds. Prior to our work, generating such a setup required at least four rounds of interactions or a trusted third party.
Multi-hop Reading Comprehension Learning Method Based on Answer Contrastive Learning

Multi-hop reading comprehension generally requires the model to give the answer and complete the prediction of supporting facts. However, previous works mainly focus on the interaction between question and context, and ignore the problem that many entities or short spans in sentences are similar to the true answer, so they do not take advantage of the differentiation information between true and plausible answers. To solve the above problems, we propose a learning method based on answer contrastive learning for multi-hop reading comprehension, which makes full use of answer judgment information to reduce the interference of confusing information to the model. Specifically, similar entity and random span data augmentation methods are proposed firstly from the perspective of answer for contrastive learning. Secondly, we implement multi-task joint learning by combining answer contrastive learning and graph neural network model through a shared encoder, and use several subtasks to mine shared information to assist in answer extraction and supporting fact prediction. Especially, the learning method forces the model to pay more attention to the true answer information through answer contrastive learning, which helps the model distinguish the start and end positions of answers. We validate our proposed learning method on the HotpotQA dataset, and the experimental results show that it performs better than the competitive baselines on several evaluation metrics.
Multi-path Based Self-adaptive Cross-lingual Summarization

Cross-lingual summarization aims to generate a summary in one language from a document in another language. This research has garnered significant attention in recent studies, addressing the need for integrating diverse language sources. However, there is still room for improvement in fully fusing these sources, particularly in the context of readily available multi-parallel language samples. In this paper, we propose a novel model that employs a multi-path approach to adjust the model structure according to the specific information of the sample while facilitating cooperation between different languages and generation tasks. We utilize a language fusion module to integrate the information from various languages, followed by a generation fusion module that enables specific decoders to share this information. Specifically, we introduce a self-adaptive method for selecting and combining different paths between decoder layers based on the sample’s context. Experimental results on English, French, and Spanish cross-lingual summarization tasks demonstrate that our proposed method outperforms established baselines.
Distant Supervision Relation Extraction with Improved PCNN and Multi-level Attention

In the research field of relation extraction, a large amount of labeled training data can be quickly obtained through distant supervision. However, distant supervision will be inevitably accompanied by the problem of wrong labelling, and the noisy data can substantially hurt the performance of relation extraction model. Previous studies mainly focus on de-noising by designing neural networks with intra-bag or inter-bag attention, but do not sufficiently take noise interference at different levels into account. Moreover, the conventional approach Piecewise Convolutional Neural Network (PCNN) encodes sentences based on an assumption that each segmented feature contributes equally to the relation, which is unreasonable. To alleviate these issues, we propose a distant supervision relation extraction model with Improved PCNN (I-PCNN) and multi-level attention. By incorporating word-level, sentence-level and bag-level attention into the model, it can effectively reduce the noisy data in a data set. Besides, it also enhances PCNN by augmenting self-attention, which can promote the encoding quality of sentences. Experiments conducted on New York Times (NYT) data set show that the proposed model evidently outperforms several baseline models in terms of the metrics $$Precision$$ , $$Recall$$ , $$AUC$$ , and $$P@N$$ .
FedDroidADP: An Adaptive Privacy-Preserving Framework for Federated-Learning-Based Android Malware Classification System

Federated-Learning-based Android malware classification framework has attracted much attention due to its privacy-preserving and multi-party joint modeling. However, research shows indirect privacy inferences from curious central servers threaten this framework. Adding noise to the model parameters to limit the adversary’s inference to sensitive knowledge is widely used to prevent this threat. Still, it dramatically reduces the classification performance of the model. In response to this challenge, we propose a privacy-preserving framework FedDroidADP, which can adapt to the law of privacy risk distribution to protect the privacy of FL-based Android malware classifier users while maintaining model utility. First, we estimate the privacy risk of Android users in the classification system by calculating the mutual information between the sharing gradient and the user’s sensitive information (Such as the category of the user’s app and malware). Then, we designed an adaptive differential privacy protection mechanism ADP according to the distribution law of the privacy risk in time and space dimensions. The mechanism calculates the added lightweight noise required to protect the user’s sensitive information (to a certain extent) in a fine-grained manner to trade off model privacy and utility during the training of Android malware classification models. Extensive experiments on the Androzoo dataset show that FedDroidADP’s ability to protect user’s sensitive information is superior to the baseline differential privacy methods and achieves better model utility (about 8% higher classification accuracy) in the same privacy budget.
How to Recover a Secret with O(n) Additions

Threshold cryptography is typically based on the idea of secret-sharing a private-key $$s\in F$$ "in the exponent" of some cryptographic group G, or more generally, encoding s in some linearly homomorphic domain. In each invocation of the threshold system (e.g., for signing or decrypting) an "encoding" of the secret is being recovered and so the complexity, measured as the number of group multiplications over G, is equal to the number of F-additions that are needed to reconstruct the secret. Motivated by this scenario, we initiate the study of n-party secret-sharing schemes whose reconstruction algorithm makes a minimal number of additions. The complexity of existing schemes either scales linearly with $$n\log |F|$$ (e.g., Shamir, CACM'79) or, at least, quadratically with n independently of the size of the domain F (e.g., Cramer-Xing, EUROCRYPT '20). This leaves open the existence of a secret sharing whose recovery algorithm can be computed by performing only O(n) additions. We resolve the question in the affirmative and present such a near-threshold secret sharing scheme that provides privacy against unauthorized sets of density at most $$\tau _p$$ , and correctness for authorized sets of density at least $$\tau _c$$ , for any given arbitrarily close constants $$\tau _p<\tau _c$$ . Reconstruction can be computed by making at most O(n) additions and, in addition, (1) the share size is constant, (2) the sharing procedure also makes only O(n) additions, and (3) the scheme is a blackbox secret-sharing scheme, i.e., the sharing and reconstruction algorithms work universally for all finite abelian groups F. Prior to our work, no such scheme was known even without features (1)–(3) and even for the ramp setting where $$\tau _p$$ and $$\tau _c$$ are far apart. As a by-product, we derive the first blackbox near-threshold secret-sharing scheme with linear-time sharing. We also present several concrete instantiations of our approach that seem practically efficient (e.g., for threshold discrete-log-based signatures). Our constructions are combinatorial in nature. We combine graph-based erasure codes that support "peeling-based" decoding with a new randomness extraction method that is based on inner-product with a small-integer vector. We also introduce a general concatenation-like transform for secret-sharing schemes that allows us to arbitrarily shrink the privacy-correctness gap with a minor overhead. Our techniques enrich the secret-sharing toolbox and, in the context of blackbox secret sharing, provide a new alternative to existing number-theoretic approaches.
Prouff and Rivain’s Formal Security Proof of Masking, Revisited

Masking is a counter-measure that can be incorporated to software and hardware implementations of block ciphers to provably secure them against side-channel attacks. The security of masking can be proven in different types of threat models. In this paper, we are interested in directly proving the security in the most realistic threat model, the so-called noisy leakage adversary, that captures well how real-world side-channel adversaries operate. Direct proofs in this leakage model have been established by Prouff & Rivain at Eurocrypt 2013, Dziembowski et al. at Eurocrypt 2015, and Prest et al. at Crypto 2019. These proofs are complementary to each other, in the sense that the weaknesses of one proof are fixed in at least one of the others, and conversely. These weaknesses concerned in particular the strong requirements on the noise level and the security parameter to get meaningful security bounds, and some requirements on the type of adversary covered by the proof—i.e., chosen or random plaintexts. This suggested that the drawbacks of each security bound could actually be proof artifacts. In this paper, we solve both issues, by revisiting Prouff & Rivain's approach.
Best of Both Worlds

This work introduces the notion of secure multiparty computation: MPC with fall-back security. Fall-back security for an n-party protocol is defined with respect to an adversary structure $$\mathcal{Z}$$ wherein security is guaranteed in the presence of both a computationally unbounded adversary with adversary structure $$\mathcal{Z}$$ , and a computationally bounded adversary corrupting an arbitrarily large subset of the parties. This notion was considered in the work of Chaum (Crypto 89) via the Spymaster's double agent problem where he showed a semi-honest secure protocol for the honest majority adversary structure. Our first main result is a compiler that can transform any n-party protocol that is semi-honestly secure with statistical security tolerating an adversary structure $$\mathcal{Z}$$ to one that (additionally) provides semi-honest fall-back security w.r.t $$\mathcal{Z}$$ . The resulting protocol has optimal round complexity, up to a constant factor, and is optimal in assumptions and the adversary structure. Our second result fully characterizes when malicious fall-back security is feasible. More precisely, we show that malicious fallback secure protocol w.r.t $$\mathcal{Z}$$ exists if and only if $$\mathcal{Z}$$ admits unconditional MPC against a semi-honest adversary (namely, iff $$\mathcal{Z}\in \mathcal{Q}^2$$ ).
Revisiting Cycles of Pairing-Friendly Elliptic Curves

A recent area of interest in cryptography is recursive composition of proof systems. One of the approaches to make recursive composition efficient involves cycles of pairing-friendly elliptic curves of prime order. However, known constructions have very low embedding degrees. This entails large parameter sizes, which makes the overall system inefficient. In this paper, we explore 2-cycles composed of curves from families parameterized by polynomials, and show that such cycles do not exist unless a strong condition holds. As a consequence, we prove that no 2-cycles can arise from the known families, except for those cycles already known. Additionally, we show some general properties about cycles, and provide a detailed computation on the density of pairing-friendly cycles among all cycles.
Multi-party Homomorphic Secret Sharing and Sublinear MPC from Sparse LPN

Over the past few years, homomorphic secret sharing (HSS) emerged as a compelling alternative to fully homomorphic encryption (FHE), due to its feasibility from an array of standard assumptions and its potential efficiency benefits. However, all known HSS schemes, with the exception of schemes built from FHE or indistinguishability obfuscation (iO), can only support two parties. In this work, we give the first construction of a multi-party HSS scheme for a non-trivial function class, from an assumption not known to imply FHE. In particular, we construct an HSS scheme for an arbitrary number of parties with an arbitrary corruption threshold, supporting evaluations of multivariate polynomials of degree $$\log / \log \log $$ over arbitrary finite fields. As a consequence, we obtain a secure multiparty computation (MPC) protocol for any number of parties, with (slightly) sub-linear per-party communication of roughly $$O(S/\log \log S)$$ bits when evaluating a layered Boolean circuit of size S. Our HSS scheme relies on the sparse Learning Parity with Noise (LPN) assumption, a standard variant of LPN with a sparse public matrix that has been studied and used in prior works. Thanks to this assumption, our construction enjoys several unique benefits. In particular, it can be built on top of any linear secret sharing scheme, producing noisy output shares that can be error-corrected by the decoder. This yields HSS for low-degree polynomials with optimal download rate. Unlike prior works, our scheme also has a low computation overhead in that the per-party computation of a constant degree polynomial takes O(M) work, where M is the number of monomials.
Multi-Dimensional Graph Rule Learner

Knowledge graph completion plays a pivotal role in the era of artificial intelligence. To harness the interpretability benefits of logical rules, we propose a cross-level position constraint template based on graph path feature learning (GPFL). For improving prediction performance, we place significant emphasis on the rule evaluation stage, specifically how to effectively learn the rules. To this end, we propose a Multi-Dimensional Graph Rule Learner (MDGRL) that calculates features from different dimensions for every rule. We assign weights to features of different dimensions based on their rule learning abilities and integrate them into rule learning. Our experiments demonstrate effectiveness across various dataset sizes, showcasing improved prediction performance in rule evaluation. The fusion of features substantially aids in enhancing prediction performance.
Secure Multiparty Computation from Threshold Encryption Based on Class Groups

We construct the first actively-secure threshold version of the cryptosystem based on class groups from the so-called CL framework (Castagnos and Laguillaumie, 2015). We show how to use our threshold scheme to achieve general universally composable (UC) secure multiparty computation (MPC) with only transparent set-up, i.e., with no secret trapdoors involved. On the way to our goal, we design new zero-knowledge (ZK) protocols with constant communication complexity for proving multiplicative relations between encrypted values. This allows us to use the ZK proofs to achieve MPC with active security with only a constant factor overhead. Finally, we adapt our protocol for the so called "You-Only-Speak-Once" (YOSO) setting, which is a very promising recent approach for performing MPC over a blockchain. This is possible because our key generation protocol is simpler and requires significantly less interaction compared to previous approaches: in particular, our new key generation protocol allows the adversary to bias the public key, but we show that this has no impact on the security of the resulting cryptosystem.
Black-Box Adversarial Attack on Graph Neural Networks Based on Node Domain Knowledge

The sensitivity of Graph Neural Networks (GNNs) to their input graph data has drawn increasing attention to adversarial graphs. Given the widespread application of GNNs in various graph tasks, it is particularly important to study the principles and implementation of graph adversarial attacks for understanding the robustness of GNNs. Previous studies have attempted to reduce the prediction accuracy of GNNs by adding small perturbations to the graph structure or node features. However, these methods typically limit the perturbation strength within a small budget and fix the perturbation budget to a constant value when perturbing the graph structure or node features. In downstream node classification tasks, the required perturbation strengths to misclassify different nodes vary. Therefore, it is important to take domain knowledge of nodes or edges into account when setting the perturbation vector. To address this issue, we propose a special adversarial graph called DK-AdvGraph, where we meticulously tailor the perturbation vector of adversarial graphs in a highly limited black-box setting. Additionally, to better confuse GNNs, we ensure a higher similarity between nodes after perturbation while setting the perturbation vector. Our extensive experimental results demonstrate that the proposed DK-AdvGraph has practical significance in promoting the progress of GNNs in considering graph domain knowledge.
Revisiting Security Estimation for LWE with Hints from a Geometric Perspective

The Distorted Bounded Distance Decoding Problem ( $$\textsf{DBDD}$$ ) was introduced by Dachman-Soled et al. [Crypto '20] as an intermediate problem between $$\textsf{LWE}$$ and unique-SVP ( $${\textsf{uSVP}}$$ ). They presented an approach that reduces an $$\textsf{LWE}$$ instance to a $$\textsf{DBDD}$$ instance, integrates side information (or "hints") into the $$\textsf{DBDD}$$ instance, and finally reduces it to a $${\textsf{uSVP}}$$ instance, which can be solved via lattice reduction. They showed that this principled approach can lead to algorithms for side-channel attacks that perform better than ad-hoc algorithms that do not rely on lattice reduction. The current work focuses on new methods for integrating hints into a $$\textsf{DBDD}$$ instance. We view hints from a geometric perspective, as opposed to the distributional perspective from the prior work. Our approach provides the rigorous promise that, as hints are integrated into the $$\textsf{DBDD}$$ instance, the correct solution remains a lattice point contained in the specified ellipsoid. We instantiate our approach with two new types of hints: (1) Inequality hints, corresponding to the region of intersection of an ellipsoid and a halfspace; (2) Combined hints, corresponding to the region of intersection of two ellipsoids. Since the regions in (1) and (2) are not necessarily ellipsoids, we replace them with ellipsoidal approximations that circumscribe the region of intersection. Perfect hints are reconsidered as the region of intersection of an ellipsoid and a hyperplane, which is itself an ellipsoid. The compatibility of "approximate," "modular," and "short vector" hints from the prior work is examined. We apply our techniques to the decryption failure and side-channel attack settings. We show that "inequality hints" can be used to model decryption failures, and that our new approach yields a geometric analogue of the "failure boosting" technique of D'anvers et al. [ePrint,'18]. We also show that "combined hints" can be used to fuse information from a decryption failure and a side-channel attack, and provide rigorous guarantees despite the data being non-Gaussian. We provide experimental data for both applications. The code that we have developed to implement the integration of hints and hardness estimates extends the Toolkit from prior work and has been released publicly.