Reviewerly/exHarmony huggingface dataset:
Streaming Functional Encryption

We initiate the study of streaming functional encryption (sFE) which is designed for scenarios in which data arrives in a streaming manner and is computed on in an iterative manner as the stream arrives. Unlike in a standard functional encryption (FE) scheme, in an sFE scheme, we (1) do not require the entire data set to be known at encryption time and (2) allow for partial decryption given only a prefix of the input. More specifically, in an sFE scheme, we can sequentially encrypt each data point $$x_i$$ in a stream of data $$x = x_1\ldots x_n$$ as it arrives, without needing to wait for all n values. We can then generate function keys for streaming functions which are stateful functions that take as input a message $$x_i$$ and a state $$\textsf{st}_i$$ and output a value $$y_i$$ and the next state $$\textsf{st}_{i+1}$$ . For any $$k \le n$$ , a user with a function key for a streaming function f can learn the first k output values $$y_1\ldots y_k$$ where $$(y_i, \textsf{st}_{i+1}) = f(x_i, \textsf{st}_i)$$ and $$\textsf{st}_1 = \bot $$ given only ciphertexts for the first k elements $$x_1\ldots x_k$$ . In this work, we introduce the notion of sFE and show how to construct it from FE. In particular, we show how to achieve a secure sFE scheme for $$\mathsf {P/Poly}$$ from a compact, secure FE scheme for $$\mathsf {P/Poly}$$ , where our security notion for sFE is similar to standard FE security except that we require all function queries to be made before the challenge ciphertext query. Furthermore, by combining our result with the FE construction of Jain, Lin, and Sahai (STOC, 2022), we show how to achieve a secure sFE scheme for $$\mathsf {P/Poly}$$ from the polynomial hardness of well-studied assumptions.
Round-Optimal Black-Box MPC in the Plain Model

We give the first construction of a (fully) black-box round-optimal secure multiparty computation protocol in the plain model. Our protocol makes black-box use of a sub-exponentially secure two-message statistical sender private oblivious transfer (SSP-OT), which in turn can be based on (sub-exponential variants of) most of the standard cryptographic assumptions known to imply public-key cryptography.
Advancing Domain Adaptation of BERT by Learning Domain Term Semantics

Pre-trained Language Models, such as BERT, have recently experienced a significant advancement, enhancing state-of-the-art performance across various Natural Language Processing (NLP) tasks. However, these models yield an unsatisfactory results in domain scenarios, particularly in specialized fields like biomedical contexts, where they cannot amass sufficient semantics of domain terms. To tackle this problem, we present a semantic learning method for BERT, focusing on the biomedical domain, to acquire and inject biomedical term semantics. Specifically, we first use BERT to encode the definitions of biomedical terms, acquiring their semantics and storing them as embeddings. Next, we design a contrastive learning task based on these embeddings to inject semantics, facilitating the transfer of domain term semantics from term embeddings to BERT’s vocabulary. This process narrows the semantic gap between the original vocabulary and domain terms in the embedding space. We evaluate our method on both general and biomedical NLP tasks, and experimental results demonstrate a significant improvement in BERT’s performance across all biomedical NLP tasks without affecting its performance on general tasks.
Local-Global Fusion Augmented Graph Contrastive Learning Based on Generative Models

Recently, graph contrastive learning (GCL) has emerged as a dominant technique for unsupervised graph representation learning. Existing GCL-based approaches typically adopt data augmentation to generate two contrastive views, and maximize the similarity between representations that share the same semantics in these two views. However, there are two weaknesses in existing methods: 1) They are highly dependent on the selection of augmentation modes, such as random node and edge perturbation as well as node feature masking, which may lead to changes in the structure and semantic information of the graph. 2) Existing augmentation strategies fail to adequately model local and global information. Graph-based generative models can use graph topology and semantic information to reconstruct node features, so as to avoid random perturbation of features. Therefore, we design a novel graph contrastive learning approach based on generative model augmentation that minimizes damage to the original graph and incorporates local-global information into the augmented view. Specifically, we first obtain the diffusion graph with global information by diffusion method. Based on the structural and semantic information of the original graph and the diffused graph, local and global augmentations are performed using generative models respectively. Then, we fuse the local and global information into the final augmented view. Extensive experiments illustrate the superior performance of model over state-of-the-art methods.
When Messages Are Keys: Is HMAC a Dual-PRF?

In Internet security protocols including TLS 1.3, KEMTLS, MLS and Noise, $$\textsf{HMAC}$$ is being assumed to be a dual-PRF, meaning a PRF not only when keyed conventionally (through its first input), but also when "swapped" and keyed (unconventionally) through its second (message) input. We give the first in-depth analysis of the dual-PRF assumption on $$\textsf{HMAC}$$ . For the swap case, we note that security does not hold in general, but completely characterize when it does; we show that $$\textsf{HMAC}$$ is swap-PRF secure if and only if keys are restricted to sets satisfying a condition called feasibility, that we give, and that holds in applications. The sufficiency is shown by proof and the necessity by attacks. For the conventional PRF case, we fill a gap in the literature by proving PRF security of $$\textsf{HMAC}$$ for keys of arbitrary length. Our proofs are in the standard model, make assumptions only on the compression function underlying the hash function, and give good bounds in the multi-user setting. The positive results are strengthened through achieving a new notion of variable key-length PRF security that guarantees security even if different users use keys of different lengths, as happens in practice.
An Improved Cross-Validated Adversarial Validation Method

As a widely-used strategy among Kaggle competitors, adversarial validation provides a novel selection framework of a reasonable training and validation sets. An adversarial validation heavily depends on an accurate identification of the difference between the distributions of the training and test sets released in a Kaggle competition. However, the typical adversarial validation merely uses a K-fold cross-validated point estimator to measure the difference regardless of the variation of the estimator. Therefore, the typical adversarial validation tends to produce unpromising false positive conclusions. In this study, we reconsider the adversarial validation from a perspective of algorithm comparison. Specifically, we formulate the adversarial validation into a comparison task of a well-trained classifier with a random-guessing classifier on an adversarial data set. Then, we investigate the state-of-the-art algorithm comparison methods to improve the adversarial validation method for reducing false positive conclusions. We conducted sufficient simulated and real-world experiments, and we showed the recently-proposed $$5\times 2$$ BCV McNemar’s test can significantly improve the performance of the adversarial validation method.
On Perfect Linear Approximations and Differentials over Two-Round SPNs

Recent constructions of (tweakable) block ciphers with an embedded cryptographic backdoor relied on the existence of probability-one differentials or perfect (non-)linear approximations over a reduced-round version of the primitive. In this work, we study how the existence of probability-one differentials or perfect linear approximations over two rounds of a substitution-permutation network can be avoided by design. More precisely, we develop criteria on the s-box and the linear layer that guarantee the absence of probability-one differentials for all keys. We further present an algorithm that allows to efficiently exclude the existence of keys for which there exists a perfect linear approximation.
Modal Interaction-Enhanced Prompt Learning by Transformer Decoder for Vision-Language Models

In the current multimodal retrieval field, CoOp is the preferred approach among many models due to its simplicity and powerful adaptive capability. However, CoOp mainly concentrates on optimizing prompt execution contrast learning and does not consider image-text interactions. In this work, we propose a prompt tuning method for simulating image-text interaction based on CoOp: Decoding context optimization (DeCoOp). Through extensive experiments on 11 image classification datasets, 7 datasets under the few-shot setting and all 11 datasets under the zero-shot setting are ahead of CoOp in our method. Experiments on four target datasets of ImageNet show a model performance improvement of more than 10%, demonstrating that our approach substantially outperforms the baseline model CoOp in terms of point domain generalization and robustness. In addition, ablation experiments performed on three representative datasets confirmed the effectiveness and further improvement of the accuracy of DeCoOp. Finally, experiments are performed on 11 datasets using different visual backbones, and it is not difficult to find that the gap between our approach and hand-crafted prompts is large in all architectures and shows better performance than CoOp.
Twin Column Parity Mixers and Gaston

We introduce a new type of mixing layer for the round function of cryptographic permutations, called circulant twin column parity mixer (CPM), that is a generalization of the mixing layers in Keccak- $$f$$ and Xoodoo. While these mixing layers have a bitwise differential branch number of 4 and a computational cost of 2 (bitwise) additions per bit, the circulant twin CPMs we build have a bitwise differential branch number of 12 at the expense of an increase in computational cost: depending on the dimension this ranges between 3 and 3.34 XORs per bit. Our circulant twin CPMs operate on a state in the form of a rectangular array and can serve as mixing layer in a round function that has as non-linear step a layer of S-boxes operating in parallel on the columns. When sandwiched between two ShiftRow-like mappings, we can obtain a columnwise branch number of 12 and hence it guarantees 12 active S-boxes per two rounds in differential trails. Remarkably, the linear branch numbers (bitwise and columnwise alike) of these mappings is only 4. However, we define the transpose of a circulant twin CPM that has linear branch number of 12 and a differential branch number of 4. We give a concrete instantiation of a permutation using such a mixing layer, named Gaston. It operates on a state of $$5 \times 64$$ bits and uses $$\chi $$ operating on columns for its non-linear layer. Most notably, the Gaston round function is lightweight in that it takes as few bitwise operations as the one of NIST lightweight standard Ascon. We show that the best 3-round differential and linear trails of Gaston have much higher weights than those of Ascon. Permutations like Gaston can be very competitive in applications that rely for their security exclusively on good differential properties, such as keyed hashing as in the compression phase of Farfalle.
CoMeta: Enhancing Meta Embeddings with Collaborative Information in Cold-Start Problem of Recommendation

The cold-start problem is quite challenging for existing recommendation models. Specifically, for the new items with only a few interactions, their ID embeddings are trained inadequately, leading to poor recommendation performance. Some recent studies introduce meta learning to solve the cold-start problem by generating meta embeddings for new items as their initial ID embeddings. However, we argue that the capability of these methods is limited, because they mainly utilize item attribute features which only contain little information, but ignore the useful collaborative information contained in the ID embeddings of users and old items. To tackle this issue, we propose CoMeta to enhance the meta embeddings with the collaborative information. CoMeta consists of two submodules: B-EG and S-EG. Specifically, for a new item: B-EG calculates the similarity-based weighted sum of the ID embeddings of old items as its base embedding; S-EG generates its shift embedding not only with its attribute features but also with the average ID embedding of the users who interacted with it. The final meta embedding is obtained by adding up the base embedding and the shift embedding. We conduct extensive experiments on two public datasets. The experimental results demonstrate both the effectiveness and the compatibility of CoMeta.
Knowledge-Aware Two-Stream Decoding for Outline-Conditioned Chinese Story Generation

Generating a coherent and reasonable story for a given story outline, i.e., outline-conditioned story generation, is an important and challenging task. The key challenges of the task lie in how to ensure that the majority of the story outline points appear in the generated story sufficiently and expand the source of information for the story outline effectively, these challenges are still under-explored by prior works, especially for outline-conditioned Chinese story generation. In this paper, we propose a novel outline-conditioned Chinese story generation framework that utilizes a two-stream decoding mechanism to make sure most of the points listed in the outlines of the stories are included in the generated stories by training the generative model and generating the stories twice. Moreover, we enlarge the outline points by incorporating external knowledge from a Chinese commonsense knowledge base to generate various Chinese stories. Extensive experiments show that our framework outperforms the state-of-the-art Chinese generation models on several evaluation metrics, demonstrating the importance of the two-stream decoding mechanism and the necessity of incorporating extra Chinese knowledge into the story outline for generating more diverse Chinese stories.
A Graph Neural Network for Cross-domain Recommendation Based on Transfer and Inter-domain Contrastive Learning

Cross-domain recommendation (CDR) is an effective method to deal with the problem of data sparsity in recommender systems. However, most of the existing CDR methods belong to single-target CDR, which only improve the recommendation effect of the target domain without considering the effect of the source domain. Meanwhile, the existing dual-target or multi-target CDR methods do not consider the differences between domains during the feature transfer. To address these problems, this paper proposes a graph neural network for CDR based on transfer and inter-domain contrastive learning (TCLCDR). Firstly, user-item graphs of two domains are constructed, and data from both domains are used to alleviate the problem of data sparsity. Secondly, a graph convolutional transfer layer is introduced to make the information of the two domains transfer bidirectionally and alleviate the problem of negative transfer. Finally, contrastive learning is performed on the overlapping users or items in the two domains, and the self-supervised contrastive learning task and supervised learning task are jointly trained to alleviate the differences between the two domain.
The Pseudorandom Oracle Model and Ideal Obfuscation

We introduce a new idealized model of hash functions, which we refer to as the pseudorandom oracle (Pr $$\mathcal {O}$$ ) model. Intuitively, it allows us to model cryptosystems that use the code of an ideal hash function in a non-black-box way. Formally, we model hash functions via a combination of a pseudorandom function (PRF) family and an ideal oracle. A user can initialize the hash function by choosing a PRF key k and mapping it to a public handle h using the oracle. Given the handle h and some input x, the oracle can also be called to evaluate the PRF at x with the corresponding key k. A user who chooses the PRF key k therefore has a complete description of the hash function and can use its code in non-black-box constructions, while an adversary, who just gets the handle h, only has black-box access to the hash function via the oracle. As our main result, we show how to construct ideal obfuscation in the Pr $$\mathcal {O}$$ model, starting from functional encryption (FE), which in turn can be based on well-studied polynomial hardness assumptions. In contrast, we know that ideal obfuscation cannot be instantiated in the basic random oracle model under any assumptions. We believe our result provides heuristic justification for the following: (1) most natural security goals implied by ideal obfuscation can be achieved in the real world; (2) obfuscation can be constructed from FE at polynomial security loss. We also discuss how to interpret our result in the Pr $$\mathcal {O}$$ model as a construction of ideal obfuscation using simple hardware tokens or as a way to bootstrap ideal obfuscation for PRFs to that for all functions.
Low Redundancy Learning for Unsupervised Multi-view Feature Selection

Multi-view feature selection is an important research direction in multi-view learning. Most of the existing multi-view feature selection methods focus on the correlation between features and data category structure, while ignoring the redundancy between features. In this paper, we propose a multi-view feature selection method based on low redundancy learning, which introduces and automatically assigns the weight of feature redundancy in each view to the projection space matrix. Subsequently, by applying $$l_{2,1}$$ norm to the projection space matrix to constrain row sparsity, the feature subsets with high correlation and low redundancy can be selected. In order to make full use of the consistency of multiple views, we also utilize spectral analysis to learn the potential category structure of each view, and minimize the difference between single-view category structure and consensus clustering indication matrix. Finally, an alternating iterative updating method is presented to solve the optimization problem. Experiments on different public multi-view data sets verify the effectiveness of the proposed method.
Dialogue State Tracking with a Dialogue-Aware Slot-Level Schema Graph Approach

Dialogue State Tracking (DST) aims to keep dialogue states updated over the whole dialogue. Recently, slot self-attention mechanism and token-level schema graph are both proposed to capture slot relations based on prior knowledge or human experience, avoiding the independent prediction of slot values. However, they fall short in modeling the correlations among slots across domain, and the dialogue history encoding method injects noises into the slot representations. To address these issues, we propose a novel slot-level schema graph to involve high cooccurrence slot relations across domain. A two layers network is then adopted to force slots to pay attention only on the relevant dialogue context and related slots successively. We make a further comparison study in modeling slot relation to quantify that the improvement of our schema graph is superior to slot self-attention. Empirical results on benchmark datasets (i.e., MultiWOZ 2.0, MultiWOZ 2.1, and MultiWOZ 2.4) show that our approach outperforms strong baselines in both predefined ontology-based DST and open vocabulary-based DST methods.
Cryptography with Weights: MPC, Encryption and Signatures

The security of many powerful cryptographic systems such as secure multiparty computation, threshold encryption, and threshold signatures rests on trust assumptions about the parties. The de-facto model treats all parties equally and requires that a certain fraction of the parties are honest. While this paradigm of one-person-one-vote has been very successful over the years, current and emerging practical use cases suggest that it is outdated. In this work, we consider weighted cryptosystems where every party is assigned a certain weight and the trust assumption is that a certain fraction of the total weight is honest. This setting can be translated to the standard setting (where each party has a unit weight) via virtualization. However, this method is quite expensive, incurring a multiplicative overhead in the weight. We present new weighted cryptosystems with significantly better efficiency: our proposed schemes incur only an additive overhead in weights. Our WRSS is based on the Chinese remainder theorem-based secret-sharing scheme. Interestingly, this secret-sharing scheme is non-linear and only achieves statistical privacy. These distinct features introduce several technical hurdles in applications to MPC and threshold cryptosystems. We resolve these challenges by developing several new ideas.
Practical Settlement Bounds for Longest-Chain Consensus

Nakamoto's longest-chain consensus paradigm now powers the bulk of the world's cryptocurrencies and distributed finance infrastructure. An emblematic property of longest-chain consensus is that it provides probabilistic settlement guarantees that strengthen over time. This makes the exact relationship between settlement error and settlement latency a critical aspect of the protocol that both users and system designers must understand to make informed decisions. A recent line of work has finally provided a satisfactory rigorous accounting of this relationship for proof-of-work longest-chain protocols, but those techniques do not appear to carry over to the proof-of-stake setting. This article develops a new analytic approach for establishing such settlement guarantees that yields explicit, rigorous settlement bounds for proof-of-stake longest-chain protocols, placing them on equal footing with their proof-of-work counterparts. Our techniques apply with some adaptations to the proof-of-work setting where they provide improvements to the state-of-the-art settlement bounds for proof-of-work protocols.
Candidate-Aware Attention Enhanced Graph Neural Network for News Recommendation

Facing the daily explosive growth of the news quantity, personalized news recommendation for users becomes an increasingly challenging problem. Modeling user interests is critical for accurate news recommendation. Most of existing methods are often lack of comprehensiveness because they do not fully consider the higher-order structure between user interaction and the news, the variation of sequences, and the importance of candidate news to user modeling. In this paper, we propose Candidate-aware Attention Enhanced Graph Neural Network for News Recommendation (GNNR), which encodes high-order connections into the representation of news through information propagation along the graph. And then combine obtained representations with representations from news content. For news representations, we learn click news and candidate news content information embedding. Then we combine it with the information from high-order structure in the graph via a graph neural network. This allows node information and topology to be integrated naturally, and dependencies between nodes can be effectively modeled. In addition, to address users’ diverse interests, we also design an attention module to dynamically aggregate a user’s history with respect to current candidate news. Extensive experiments on benchmark datasets show that our approach can effectively improve performance in news recommendation.