Reviewerly/exHarmony huggingface dataset:
Shorter and Faster Identity-Based Signatures with Tight Security in the (Q)ROM from Lattices

We provide identity-based signature (IBS) schemes with tight security against adaptive adversaries, in the (classical or quantum) random oracle model (ROM or QROM), in both unstructured and structured lattices, based on the SIS or RSIS assumption. These signatures are short (of size independent of the message length). Our schemes build upon a work from Pan and Wagner (PQCrypto’21) and improve on it in several ways. First, we prove their transformation from non-adaptive to adaptive IBS in the QROM. Then, we simplify the parameters used and give concrete values. Finally, we simplify the signature scheme by using a non-homogeneous relation, which helps us reduce the size of the signature and get rid of one costly trapdoor delegation. On the whole, we get better security bounds, shorter signatures and faster algorithms.
HS-Based Error Correction Algorithm for Noisy Binary GCD Side-Channel Sequences

The secure implementation of the Greatest Common Divisor (GCD) algorithm is fundamental for many cryptographic schemes. The binary GCD algorithm has a highly input-dependent behavior. Therefore, we must carefully implement the binary GCD used in cryptographic systems. However, it has been noted that the binary GCD algorithm implemented in OpenSSL 1.1.0-1.1.0h and 1.0.2b-1.0.2o is not secure. Aldaya et al. presented this vulnerability at CHES2019. They also proposed a side-channel attack to collect sequences of operations performed by the binary GCD algorithm and an error correction algorithm (AGTB algorithm) to recover the LSBs of secret keys from the noisy sequences. In this paper, we propose an error correction algorithm that, like the AGTB algorithm, focuses on only a single type of error. We evaluate our algorithm using numerical experiments that reveal that our algorithm achieves a higher recovery rate than the AGTB algorithm.
Steal from Collaboration: Spy Attack by a Dishonest Party in Vertical Federated Learning

Vertical federated learning (VFL) is an emerging paradigm that enables multiple companies to collaboratively train a global model without disclosing raw data. These companies have a common set of users but different features. As a privacy-preserving paradigm, VFL has gradually attracted more attention and several studies recently have investigated its privacy risks. In this work, we study a new attack path for an attacker to steal private data from collaboration which existing works have not explored. In VFL, the features of a user are distributed among multiple parties. Assume the attacker is dishonest and lacks the victim user’s data. He can speculate the missing data based on intermediate information from other parties during collaboration process. We call this attack spy attack. In this paper, we perform the first systematic study on spy attack and design two methods to execute spy attacks for the two cases where the attacker is the active party and the passive party. Evaluations with four real-world datasets demonstrate the effectiveness of our attacks. Even when the attacker’s data missing rate is 90%, most reconstruction qualities by spy attacks are considerably superior to random guessing and approach the theoretically best reconstruction performance. We further discuss possible defenses and highlight the need for designing more effective and efficient defense strategies against spy attack.
Analysis and Prevention of Averaging Attacks Against Obfuscation Protocols

Verification and traceability of supply-chain data is a common example for public analysis of confidential data. Finding the correct balance between confidentiality and utility often is anything but trivial. In order to ensure confidentiality and thus protect companies’ competitive advantages, existing approaches employ probabilistic output obfuscation. However, it is known that this form of obfuscation might render a system subject to averaging attacks. In these attacks, an adversary repeatedly queries for the same analysis and combines the probabilistic outputs, thus implementing an estimator that eliminates the obfuscation. A clear picture on the performance of such attacks is missing, information that is crucial for mitigating averaging attacks. Our contributions are threefold: First, using an existing supply-chain verification protocol (RVP) as a particularly efficient example of protocols with output obfuscation, we extensively analyze the risk posed by averaging attacks. We prove rigorously that such attacks perform exceptionally well if obfuscation is based on random values sampled independently in every query. We generalize our analysis to all protocols that employ probabilistic output obfuscation. Second, we propose the paradigm of data-dependent deterministic obfuscation (D $${^3}$$ O) to prevent such attacks. Third, we present mRVP, a D $${^3}$$ O-based version of RVP, and empirically demonstrate practicality and effectiveness of D $${^3}$$ O. The results show that our mitigations add negligible runtime overhead, do not affect accuracy, and effectively retain confidentiality.
TIDAL: Practical Collisions on State-Reduced Keccak Variants

An important tool that has contributed to collision search on Keccak /SHA3 is the Target Difference Algorithm (TDA) and its internal differential counterpart Target Internal Difference Algorithm (TIDA) which were introduced by Dinur et al. in separate works in FSE 2012 and 2013 respectively. These algorithms provide an ingenious way of extending the differential trails by one round and exploit the affine subspaces generated due to low algebraic degree of the Keccak S-box. The current work introduces TIDAL, which can extend TIDA by one more round capitalizing on linearization techniques introduced by Guo et al. in JoC. The TIDAL strategy in conjunction with a deterministic internal differential trail has been applied to Keccak variants up till 400-bit state-size and leads to practical collision attacks for most of them up to 5 rounds. In particular collisions have been confirmed for 4-round Keccak [136, 64] with a complexity of $$2^{20}$$ and on 6-round of Keccak [84,16] with a complexity of $$2^5$$ . Further, this work provides a complete characterization of all collision attacks on state-reduced variants showcasing that TIDAL covers most of the space up till 5 rounds. As state and round-reduced Keccak variants are used to realize internal states of many crypto primitives, the results presented here generate significant impact. Finally, it shows new directions for the long standing problem of state-reduced variants being difficult to be attacked.
BIKE Key-Recovery: Combining Power Consumption Analysis and Information-Set Decoding

In this paper, we present a single-trace attack on a BIKE Cortex-M4 implementation proposed by Chen et al. at CHES 2021. BIKE is a key-encapsulation mechanism, candidate to the NIST post-quantum cryptography standardisation process. We attack by exploiting the rotation function that circularly shifts an array depending on the private key. Chen et al. implemented two versions of this function, one in C and one in assembly. Our attack uses subtraces clustering combined with a combinatorial attack to recover the full private key. We obtained a high clustering accuracy in our experiments, and we provide ways to deal with the errors. We are able to recover all the private keys for the C implementation, and while the assembly version is harder to attack using our technique, we still manage to reduce BIKE Level-1 security from 128 to 65 bits for a significant proportion of the private keys.
Divide and Rule: DiFA - Division Property Based Fault Attacks on PRESENT and GIFT

The division property introduced by Todo in Crypto 2015 is one of the most versatile tools in the arsenal of a cryptanalyst which has given new insights into many ciphers primarily from an algebraic perspective. On the other end of the spectrum we have fault attacks which have evolved into the deadliest of all physical attacks on cryptosystems. The current work aims to combine these seemingly distant tools to come up with a new type of fault attack. We show how fault invariants are formed under special input division multi-sets and are independent of the fault injection location. It is further shown that the same division trail can be exploited as a multi-round Zero-Sum distinguisher to reduce the key-space to practical limits. As a proof of concept division trails of PRESENT and GIFT are exploited to mount practical key-recovery attacks based on the random nibble fault model. For GIFT-64, we are able to recover the unique master-key with 30 nibble faults with faults injected at rounds 21 and 19. For PRESENT-80, DiFA reduces the key-space from $$2^{80}$$ to $$2^{16}$$ with 15 faults in round 25 while for PRESENT-128, the unique key is recovered with 30 faults in rounds 25 and 24. This constitutes the best fault attacks on these ciphers in terms of fault injection rounds. We also report an interesting property pertaining to fault induced division trails which shows its inapplicability to attack GIFT-128. Overall, the usage of division trails in fault based cryptanalysis showcases new possibilities and reiterates the applicability of classical cryptanalytic tools in physical attacks.
Forward Security of Fiat-Shamir Lattice Signatures

Forward security is a desirable property for lattice-based signatures, which was posed by NIST PQC standardization against secret key exposures. The known methods applied an additional short basis for delegation to update the secret key, which seems quite strange for Fiat-Shamir type lattice signatures (built without short basis), then an aftermath is that larger secret keys and expensive key updating are inevitable. In this paper, we revisit the problem and obtain some interesting results. By reinterpreting the Chen et al.’s exciting observations [11], we describe a framework explicitly that unifies lattice-based FDH and Fiat-Shamir signatures under a generalized ISIS trapdoor perspective. Based on the framework, we design a trapdoor evolution algorithm (without short basis) such that the lattice dimension is fixed, which improves the Micciancio-Peikert trapdoor delegation. Then, by adapting a binary tree structure for key update, we construct a forward-secure lattice-based Fiat-Shamir signature with a smaller secret key size and efficient key evolution.
Near Collision Attack Against Grain V1

A near collision attack against the Grain v1 stream cipher was proposed by Zhang et al. in Eurocrypt 18. The attack uses the fact that two internal states of the stream cipher with very low hamming distance between them, produce similar keystream sequences which can be identified by simple statistical tests. Such internal states once found in the stream cipher simplify the task of cryptanalysis for the attacker. However this attack has recently come under heavy criticism from Derbez et al. at ToSC 2020:4, who claim that some of the assumptions made in the above paper were not correct. As a result they concluded that the attack presented by Zhang et al. when implemented would take time more than required for a brute force search. In this paper, we take another look at the near collision attack against the Grain v1 stream cipher. We avoid the techniques of the above Eurocrypt paper that have come under criticism, and independently show that a near collision attack can still be applied to Grain v1.
A Novel Automatic Technique Based on MILP to Search for Impossible Differentials

The Mixed Integer Linear Programming (MILP) is a common method of searching for impossible differentials (IDs). However, the optimality of the distinguisher should be confirmed by an exhaustive search of all input and output differences, which is clearly computationally infeasible due to the huge search space. In this paper, we propose a new technique that uses two-dimensional binary variables to model the input and output differences and characterize contradictions with constraints. In our model, the existence of IDs can be directly obtained by checking whether the model has a solution. In addition, our tool can also detect any contradictions between input and output differences by changing the position of the contradictions. Our method is confirmed by applying it to several block ciphers, and our results show that we can find 6-, 13-, and 12-round IDs for Midori-64, CRAFT, and SKINNY-64 within a few seconds, respectively. Moreover, by carefully analyzing the key schedule of Midori-64, we propose an equivalent key transform technique and construct a complete MILP model for an 11-round impossible differential attack (IDA) on Midori-64 to search for the minimum number of keys to be guessed. Based on our automatic technique, we present a new 11-round IDA on Midori-64, where 23 nibbles of keys need to be guessed, which reduces the time complexity compared to previous work. The time and data complexity of our attack are $$2^{116.59}$$ and $$2^{60}$$ , respectively. To the best of our knowledge, this is the best IDA on Midori-64 at present.
A Gapless Post-quantum Hash Proof System in the Hamming Metric

A hash proof system (HPS) is a form of implicit proof of membership to a language. Out of the very few existing post-quantum HPS, most are based on languages of ciphertexts of code-based or lattice-based cryptosystems and inherently suffer from a gap caused by the possibility for an ill-formed ciphertext to decrypt to a valid plaintext. Since this gap is inconvenient when proving the security in the universal composability framework by Canetti et al., Bettaieb et al. proposed the first gapless post-quantum HPS based on the Rank Quasi-Cyclic (RQC) cryptosystem in the rank metric while conjecturing the existence of a similar HPS in the usual Hamming metric. We solve this conjecture by designing a gapless post-quantum HPS based on the Hamming Quasi-Cyclic (HQC) cryptosystem which, in contrast to RQC, is a NIST post-quantum cryptography standardization alternate candidate. We describe a novel proof of validity for HQC ciphertexts, thereby closing the adversarial gap and present a witness encryption scheme secure in the standard model and a password-based authenticated key exchange protocol secure in the Bellare–Pointcheval–Rogaway (BPR) model.
Tiny WFP: Lightweight and Effective Website Fingerprinting via Wavelet Multi-Resolution Analysis

Network eavesdroppers can determine which website Tor users visit by analyzing encrypted traffic traces with the Website Fingerprinting (WF) attack. WF attacks based on Deep Learning, like Deep Fingerprinting (DF) outperformed traditional statistical methods by large margins and achieved state-of-the-art. However, Deep-Learning-based WF requires high computation and storage overhead, has scalability issues, and is difficult to deploy on weak attackers with limited resources. To address this challenge, we present Tiny WFP, a lightweight WF that uses wavelet-based dimensionality reduction and an efficient neural network. We conduct wavelet decomposition and discard high-frequency coefficients to reduce the feature dimension and keep the WF success rate. Our efficient neural network is a stack of depthwise separable convolution layers with a sophisticated design. Tiny WFP attains 99.2% accuracy on undefended Tor traces while being 81x smaller and 79x less computationally intensive than DF. Tiny WFP also achieves comparable performance in the presence of Tor defenses. Tiny WFP is an effective and scalable Website Fingerprinting attack that can potentially be deployed on real-life network devices, providing a solid deterrent to crimes that exploit anonymous communications.
Factors Affecting Firm Innovation: An Evidence from Vietnam

Firm innovation plays an essential role in the development of enterprises. A firm with innovation can provide products that meet the ever-changing needs of the market and the demands of consumers. From there, it is possible to improve the competitiveness of enterprises in the market, especially in the current, constantly fierce competitive environment. Therefore, building a firm innovation is a significant task for enterprises and policy makers. However, firm innovation is influenced by many internal and external factors of the enterprise. Therefore, it is necessary to identify the factors that affect the firm innovation. From this, they can offer solutions that contribute to the development of firm innovation in a better way. This article focuses on determining the factors affecting firm innovation in the specific case of enterprises operating in Vietnam. Vietnam is a developing country and a young economy with many types of enterprises. The study was carried out based on a sample of 916 enterprises in Vietnam. These research data were provided through the World Bank's Enterprises Survey in 2015. The binary logistic regression method is used. The results show that factors such as the top manager's period work experience, the competition of the firm, the access to finance, corruption, the number of employees of enterprises, and trained workers are factors affecting the firm innovation in Vietnam. Through this article, stakeholders such as businesses, local managers, and policymakers in Vietnam will have solutions to improve the firm innovation. The paper ends with a discussion of the results and implications of the study.
Named Entity Recognition over Dialog Dataset Using Pre-trained Transformers

Need of natural language processing (NLP) applications and advancement in deep learning (DL) techniques have increased the need of large amount of human readable data leading to interesting research area named entity recognition (NER), which is a sub-task of natural language processing. NER identifies and tags different real-life entities in their pre-defined categories. Pre-defined categories include person, locations, times, organizations, events, etc., depending upon dataset in hand. For different natural language processing applications such as information retrieval (IR), question answering system (QAS), text summarization (TS), and machine translation (MT), NER forms a base work. Performance of earlier NER techniques is good but requires human intervention for forming domain-specific features or rules. Performance of NER system is further improved by application of emerging deep learning models, and also use of NER in dialog system is less explored area. So, in this paper, our aim is to mention different techniques which can be applied to do NER task and fine-tune the prê-trained transformers to work on in-car dialog dataset for NER task and evaluate the performance of system.
Study of Cold-Start Product Recommendations and Its Solutions

In today's digital era, consumers rely more and more on the systems that provide them with a personalized experience. In interacting with the system, these consumers create more and more data of different types (click-through rates, items viewed, time spent, number of purchases, and other metrics.). This extensive collection of data from various users is used only to improve the personalizing experience of the users. These systems that utilize consumers’ data to create a more personalized and customized user experience are called Recommender Systems. Recommender Systems play a huge role in helping companies create a more engaging user experience. E-commerce giants like Amazon and Flipkart employ such Recommender Systems. These can learn from the user–system interaction, the likes and dislikes of users, and can promote the visibility of items that interest the user. They are also helpful in luring the customer to buy those things he would have to search for manually in the absence of such a system, which can recommend the item to a user based on his previous interactions. Streaming services like YouTube, Amazon Prime Video, and Netflix also use Recommender Systems to suggest movies/shows that the user might like based on the watch history. This study proposes a hybrid model with item–item collaborative filtering using a graph, user–user collaborative filtering based on textual reviews and ratings, and demographic data to generate accurate product recommendations that address the cold-start issue.
X-ABI: Toward Parameter-Efficient Multilingual Adapter-Based Inference for Cross-Lingual Transfer

Natural Language Inference for low-resource languages is challenging due to the unavailability of sufficient training samples for downstream tasks. This work proposes to leverage the large corpus availability of high-resource languages and transfer learning to assist low-resource languages. We propose a method, multilingual adapter-based inference (X-ABI), to interpolate through the language spaces in multilingual language models using adapters in transformers. This results in comparable accuracies on downstream tasks by efficiently training the model on available training samples from mainstream languages. We show that training only ~0.1% of the parameters on the English dataset performs 20.9% better than baseline and is comparable to the state of the art of other languages present on the XNLI dataset with 4 h of training on free Google Colab GPU.
Secure Authentication of IoT Devices Using Upgradable Smart Contract and Fog-Blockchain Technology

There are many new challenges related to the security of the Internet of Things networks due to the rapid growth of the technology. One of the main challenges is device security and authentication. Due to their limited processing and storage capabilities, the Internet of Things devices are unable to protect themselves from different attacks. Passwords or predefined keys have drawbacks that limit their uses. With high trust, integrity, and transparency, distributed ledger technology has the ability to address these issues in IoT. The solution discussed in this paper is to use fog computing and blockchain technology to authenticate IoT devices using smart tokens generated by an upgradable smart contract.
Identifying Trends Using Improved Affinity Propagation (IMAP) Clustering Algorithm on Evolving Data Stream

The clustering of an evolving data stream is always very complex as data objects are not static in nature. Identifying the real-time clusters is a task. The characteristic of the data objects in an evolving data stream is also evolving. If these evolving patterns of the data objects are labeled and defined, it may be possible to identify the context change and such repetitive change in the context will explore the trends in the evolving data stream. However, it is necessary to understand the occurrence, reoccurrence, and diminishing properties of these clusters over time. The improved affinity propagation (IMAP) clustering algorithm identifies these changes in the characteristics of the clusters over time. Continuous observation and registration of new clusters lead us to detect the trends in the evolving data stream. Analyzing these trends may be able to understand the patterns or changes in the context so that future prediction is possible. The proposed IMAP algorithm is more efficient over pre-estimation of a number of clusters and probing at right time the evolving data stream over time without any loss of information. The algorithm is also robust in identifying the outliers. The advantages of the trend analysis approach are shown via experiments on real data sets.
Comparative Analysis of Recommendation System Using Similarity Techniques

Recommendation system (RS) is an important software for information filtering system. Upper approximation algorithm apply for forming cluster from MSNBC dataset, in this paper find out more relevant similar web page using different similarity measure technique like sequence similarity–length of longest common subsequence (LLCS) and set similarity–Jaccard similarity, dice similarity, and cosine similarity for calculating of recommendation system. In this work we compare existing work technique similarity result with proposed work technique result on MSNBC dataset using hybrid similarity technique (sequence set similarity measure) according to our comparative results, the suggested system offers new users recommendations with good reliability and accuracy values.