Reviewerly/exHarmony huggingface dataset:
Anonymous Tokens with Stronger Metadata Bit Hiding from Algebraic MACs

On the one hand, the web needs to be secured from malicious activities such as bots or DoS attacks; on the other hand, such needs ideally should not justify services tracking people's activities on the web. Anonymous tokens provide a nice tradeoff between allowing an issuer to ensure that a user has been vetted and protecting the users' privacy. However, in some cases, whether or not a token is issued reveals a lot of information to an adversary about the strategies used to distinguish honest users from bots or attackers. In this work, we focus on designing an anonymous token protocol between a client and an issuer (also a verifier) that enables the issuer to support its fraud detection mechanisms while preserving users' privacy. This is done by allowing the issuer to embed a hidden (from the client) metadata bit into the tokens. We first study an existing protocol from CRYPTO 2020 which is an extension of Privacy Pass from PoPETs 2018; that protocol aimed to provide support for a hidden metadata bit, but provided a somewhat restricted security notion. We demonstrate a new attack, showing that this is a weakness of the protocol, not just the definition. In particular, the metadata bit hiding is weak in the setting where the attacker can redeem some tokens and get feedback on whether the bit extraction succeeded. We then revisit the formalism of anonymous tokens with private metadata bit, consider the more natural notion, and design a scheme which achieves it. In order to design this new secure protocol, we base our construction on algebraic MACs instead of PRFs. Our security definitions capture a realistic threat model where adversaries could, through direct feedback or side channels, learn the embedded bit when the token is redeemed. Finally, we compare our protocol with one of the CRYPTO 2020 protocols. We obtain 20% more efficient performance.
Toward Practical Lattice-Based Proof of Knowledge from Hint-MLWE

In the last decade, zero-knowledge proof of knowledge protocols have been extensively studied to achieve active security of various cryptographic protocols. However, the existing solutions simply seek zero-knowledge for both message and randomness, which is an overkill in many applications since protocols may remain secure even if some information about randomness is leaked to the adversary. We develop this idea to improve the state-of-the-art proof of knowledge protocols for RLWE-based public-key encryption and BDLOP commitment schemes. In a nutshell, we present new proof of knowledge protocols without using noise flooding or rejection sampling which are provably secure under a computational hardness assumption, called Hint-MLWE. We also show an efficient reduction from Hint-MLWE to the standard MLWE assumption. Our approach enjoys the best of two worlds because it has no computational overhead from repetition (abort) and achieves a polynomial overhead between the honest and proven languages. We prove this claim by demonstrating concrete parameters and compare with previous results. Finally, we explain how our idea can be further applied to other proof of knowledge providing advanced functionality.
Lattice-Based Authenticated Key Exchange with Tight Security

We construct the first tightly secure authenticated key exchange (AKE) protocol from lattices. Known tight constructions are all based on Diffie-Hellman-like assumptions. Thus, our protocol is the first construction with tight security from a post-quantum assumption. Our AKE protocol is constructed tightly from a new security notion for key encapsulation mechanisms (KEMs), called one-way security against checkable chosen-ciphertext attacks (OW-ChCCA). We show how an OW-ChCCA secure KEM can be tightly constructed based on the Learning With Errors assumption, leading to the desired AKE protocol. To show the usefulness of OW-ChCCA security beyond AKE, we use it to construct the first tightly bilateral selective-opening (BiSO) secure PKE. BiSO security is a stronger selective-opening notion proposed by Lai et al. (ASIACRYPT 2021).
$$ \textsf{DualMS}$$: Efficient Lattice-Based Two-Round Multi-signature with Trapdoor-Free Simulation

A multi-signature scheme allows multiple signers to jointly sign a common message. In recent years, two lattice-based two-round multi-signature schemes based on Dilithium-G were proposed: DOTT by Damgård, Orlandi, Takahashi, and Tibouchi (PKC’21) and MuSig-L by Boschini, Takahashi, and Tibouchi (Crypto’22). In this work, we propose a new lattice-based two-round multi-signature scheme called $$ \textsf{DualMS}$$ . Compared to DOTT, $$ \textsf{DualMS}$$ is likely to significantly reduce signature size, since it replaces an opening to a homomorphic trapdoor commitment with a Dilithium-G response in the signature. Compared to MuSig-L, concrete parameters show that $$ \textsf{DualMS}$$ has smaller public keys, signatures, and lower communication, while the first round cannot be preprocessed offline as in MuSig-L. The main reason behind such improvements is a trapdoor-free “dual signing simulation” of our scheme. Signature simulation of $$ \textsf{DualMS}$$ is virtually identical the normal signing procedure and does not use lattice trapdoors like DOTT and MuSig-L.
Network-Agnostic Security Comes (Almost) for Free in DKG and MPC

Distributed key generation (DKG) protocols are an essential building block for threshold cryptosystems. Many DKG protocols tolerate up to $$t_s<n/2$$ corruptions assuming a well-behaved synchronous network, but become insecure as soon as the network delay becomes unstable. On the other hand, solutions in the asynchronous model operate under arbitrary network conditions, but only tolerate $$t_a<n/3$$ corruptions, even when the network is well-behaved. In this work, we ask whether one can design a protocol that achieves security guarantees in either scenario. We show a complete characterization of network-agnostic DKG protocols, showing that the tight bound is $$t_a+2t_s <n$$ . As a second contribution, we provide an optimized version of the network-agnostic multi-party computation (MPC) protocol by Blum, Liu-Zhang and Loss [CRYPTO'20] which improves over the communication complexity of their protocol by a linear factor. Moreover, using our DKG protocol, we can instantiate our MPC protocol in the plain PKI model, i.e., without the need to assume an expensive trusted setup. Our protocols incur comparable communication complexity as state-of-the-art DKG and MPC protocols with optimal resilience in their respective purely synchronous and asynchronous settings, thereby showing that network-agnostic security comes (almost) for free.
Revisiting the Indifferentiability of the Sum of Permutations

The sum of two n-bit pseudorandom permutations is known to behave like a pseudorandom function with n bits of security. A recent line of research has investigated the security of two public n-bit permutations and its degree of indifferentiability. Mandal et al. (INDOCRYPT 2010) proved 2n/3-bit security, Mennink and Preneel (ACNS 2015) pointed out a non-trivial flaw in their analysis and re-proved $$(2n/3-\log _2(n))$$ -bit security. Bhattacharya and Nandi (EUROCRYPT 2018) eventually improved the result to n-bit security. Recently, Gunsing at CRYPTO 2022 already observed that a proof technique used in this line of research only holds for sequential indifferentiability. We revisit the line of research in detail, and observe that the strongest bound of n-bit security has two other serious issues in the reasoning, the first one is actually the same non-trivial flaw that was present in the work of Mandal et al., while the second one discards biases in the randomness influenced by the distinguisher. More concretely, we introduce two attacks that show limited potential of different approaches. We (i) show that the latter issue that discards biases only holds up to $$2^{3n/4}$$ queries, and (ii) perform a differentiability attack against their simulator in $$2^{5n/6}$$ queries. On the upside, we revive the result of Mennink and Preneel and show $$(2n/3-\log _2(n))$$ -bit regular indifferentiability security of the sum of public permutations.
Perfect MPC over Layered Graphs

The classical "BGW protocol" (Ben-Or, Goldwasser, and Wigderson, STOC 1988) shows that secure multiparty computation (MPC) among n parties can be realized with perfect full security if $$t < n/3$$ parties are corrupted. This holds against malicious adversaries in the "standard" model for MPC, where a fixed set of n parties is involved in the full execution of the protocol. However, the picture is less clear in the mobile adversary setting of Ostrovsky and Yung (PODC 1991), where the adversary may periodically "move" by uncorrupting parties and corrupting a new set of t parties. In this setting, it is unclear if full security can be achieved against an adversary that is maximally mobile, i.e., moves after every round. The question is further motivated by the "You Only Speak Once" (YOSO) setting of Gentry et al. (Crypto 2021), where not only the adversary is mobile but also each round is executed by a disjoint set of parties. Previous positive results in this model do not achieve perfect security, and either assume probabilistic corruption and a nonstandard communication model, or only realize the weaker goal of security-with-abort. The question of matching the BGW result in these settings remained open. In this work, we tackle the above two challenges simultaneously. We consider a layered MPC model, a simplified variant of the fluid MPC model of Choudhuri et al. (Crypto 2021). Layered MPC is an instance of standard MPC where the interaction pattern is defined by a layered graph of width n, allowing each party to send secret messages and broadcast messages only to parties in the next layer. We require perfect security against a malicious adversary who may corrupt at most t parties in each layer. Our main result is a perfect, fully secure layered MPC protocol with an optimal corruption threshold of $$t < n/3$$ , thus extending the BGW feasibility result to the layered setting. This implies perfectly secure MPC protocols against a maximally mobile adversary.
Security Analysis of the WhatsApp End-to-End Encrypted Backup Protocol

WhatsApp is an end-to-end encrypted (E2EE) messaging service used by billions of people. In late 2021, WhatsApp rolled out a new protocol for backing up chat histories. The E2EE WhatsApp backup protocol (WBP) allows users to recover their chat history from passwords, leaving WhatsApp oblivious of the actual encryption keys. The WBP builds upon the OPAQUE framework for password-based key exchange, which is currently undergoing standardization. While considerable efforts have gone into the design and auditing of the WBP, the complexity of the protocol's design and shortcomings in the existing security analyses of its building blocks make it hard to understand the actual security guarantees that the WBP provides. In this work, we provide the first formal security analysis of the WBP. Our analysis in the universal composability (UC) framework confirms that the WBP provides strong protection of users' chat history and passwords. It also shows that a corrupted server can under certain conditions make more password guesses than what previous analysis suggests.
How to Use (Plain) Witness Encryption: Registered ABE, Flexible Broadcast, and More

Witness encryption is a generalization of public-key encryption where the public key can be any $$\textsf{NP}$$ statement x and the associated decryption key is any witness w for x. While early constructions of witness encryption relied on multilinear maps and indistinguishability obfuscation ( $$i\mathcal {O}$$ ), recent works have provided direct constructions of witness encryption that are more efficient than $$i\mathcal {O}$$ (and also seem unlikely to yield $$i\mathcal {O}$$ ). Motivated by this progress, we revisit the possibility of using witness encryption to realize advanced cryptographic primitives previously known only in "obfustopia." In this work, we give new constructions of trustless encryption systems from plain witness encryption (in conjunction with the learning-with-errors assumption): (1) flexible broadcast encryption (a broadcast encryption scheme where users choose their own secret keys and users can encrypt to an arbitrary set of public keys); and (2) registered attribute-based encryption (a system where users choose their own keys and then register their public key together with a set of attributes with a deterministic and transparent key curator). Both primitives were previously only known from $$i\mathcal {O}$$ . We also show how to use our techniques to obtain an optimal broadcast encryption scheme in the random oracle model. Underlying our constructions is a novel technique for using witness encryption based on a new primitive which we call function-binding hash functions. Whereas a somewhere statistically binding hash function statistically binds a digest to a few bits of the input, a function-binding hash function statistically binds a digest to the output of a function of the inputs. As we demonstrate in this work, function-binding hash functions provide us new ways to leverage the power of plain witness encryption and use it as the foundation of advanced cryptographic primitives. Finally, we show how to build function-binding hash functions for the class of disjunctions of block functions from leveled homomorphic encryption; this in combination with witness encryption yields our main results.
Finding Short Integer Solutions When the Modulus Is Small

We present cryptanalysis of the inhomogenous short integer solution ( $$\textsf{ISIS}_{}$$ ) problem for anomalously small moduli $$q$$ by exploiting the geometry of BKZ reduced bases of q-ary lattices. We apply this cryptanalysis to examples from the literature where taking such small moduli has been suggested. A recent work [Espitau–Tibouchi–Wallet–Yu, CRYPTO 2022] suggests small $$q$$ versions of the lattice signature scheme Falcon and its variant Mitaka. For one small $$q$$ parametrisation of Falcon we reduce the estimated security against signature forgery by approximately 26 bits. For one small $$q$$ parametrisation of Mitaka we successfully forge a signature in 15 s.
Cuckoo Hashing in Cryptography: Optimal Parameters, Robustness and Applications

Cuckoo hashing is a powerful primitive that enables storing items using small space with efficient querying. At a high level, cuckoo hashing maps n items into b entries storing at most $$\ell $$ items such that each item is placed into one of k randomly chosen entries. Additionally, there is an overflow stash that can store at most s items. Many cryptographic primitives rely upon cuckoo hashing to privately embed and query data where it is integral to ensure small failure probability when constructing cuckoo hashing tables as it directly relates to the privacy guarantees. As our main result, we present a more query-efficient cuckoo hashing construction using more hash functions. For construction failure probability $$\epsilon $$ , the query overhead of our scheme is $$O(1 + \sqrt{\log (1/\epsilon )/\log n})$$ . Our scheme has quadratically smaller query overhead than prior works for any target failure probability $$\epsilon $$ . We also prove lower bounds matching our construction. Our improvements come from a new understanding of the locality of cuckoo hashing failures for small sets of items. We also initiate the study of robust cuckoo hashing where the input set may be chosen with knowledge of the hash functions. We present a cuckoo hashing scheme using more hash functions with query overhead $$\tilde{O}(\log \lambda )$$ that is robust against $$\textsf{poly}(\lambda )$$ adversaries. Furthermore, we present lower bounds showing that this construction is tight and that extending previous approaches of large stashes or entries cannot obtain robustness except with $$\varOmega (n)$$ query overhead. As applications of our results, we obtain improved constructions for batch codes and PIR. In particular, we present the most efficient explicit batch code and blackbox reduction from single-query PIR to batch PIR.
Error Correction and Ciphertext Quantization in Lattice Cryptography

Recent work in the design of rate $$1 - o(1)$$ lattice-based cryptosystems have used two distinct design paradigms, namely replacing the noise-tolerant encoding $$m \mapsto (q/2)m$$ present in many lattice-based cryptosystems with a more efficient encoding, and post-processing traditional lattice-based ciphertexts with a lossy compression algorithm, using a technique very similar to the technique of "vector quantization" within coding theory. We introduce a framework for the design of lattice-based encryption that captures both of these paradigms, and prove information-theoretic rate bounds within this framework. These bounds separate the settings of trivial and non-trivial quantization, and show the impossibility of rate $$1 - o(1)$$ encryption using both trivial quantization and polynomial modulus. They furthermore put strong limits on the rate of constructions that utilize lattices built by tensoring a lattice of small dimension with $$\mathbb {Z}^k$$ , which is ubiquitous in the literature. We additionally introduce a new cryptosystem, that matches the rate of the highest-rate currently known scheme, while encoding messages with a "gadget", which may be useful for constructions of Fully Homomorphic Encryption.
Neural Radiance Fields for High-Resolution Remote Sensing Novel View Synthesis

Remote sensing images play a crucial role in remote sensing target detection and 3D remote sensing modeling, and the enhancement of resolution holds significant application implications. The task of remote sensing target detection requires a substantial amount of high-resolution remote sensing images, while 3D reconstruction tasks generate denser models from diverse view perspectives. However, high-resolution remote sensing images are often limited due to their high acquisition costs, a scarcity of acquisition views, and restricted view perspective variations, which pose challenges for remote sensing tasks. In this paper, we propose an advanced method for a high-resolution remote sensing novel view synthesis by integrating attention mechanisms with neural radiance fields to address the scarcity of high-resolution remote sensing images. To enhance the relationships between sampled points and rays and to improve the 3D implicit model representation capability of the network, we introduce a point attention module and batch attention module into the proposed framework. Additionally, a frequency-weighted position encoding strategy is proposed to determine the significance of each frequency for position encoding. The proposed method is evaluated on the LEVIR-NVS dataset and demonstrates superior performance in quality assessment metrics and visual effects compared to baseline NeRF (Neural Radiance Fields) and ImMPI (Implicit Multi-plane Images). Overall, this work presents a promising approach for a remote sensing novel view synthesis by leveraging attention mechanisms and frequency-weighted position encoding.
Human Activity Recognition Method Based on Edge Computing-Assisted and GRU Deep Learning Network

Human Activity Recognition (HAR) has been proven to be effective in various healthcare and telemonitoring applications. Current HAR methods, especially deep learning, are extensively employed owing to their exceptional recognition capabilities. However, in pursuit of enhancing feature expression abilities, deep learning often introduces a trade-off by increasing Time complexity. Moreover, the intricate nature of human activity data poses a challenge as it can lead to a notable decrease in recognition accuracy when affected by additional noise. These aspects will significantly impair recognition performance. To advance this field further, we present a HAR method based on an edge-computing-assisted and GRU deep-learning network. We initially proposed a model for edge computing to optimize the energy consumption and processing time of wearable devices. This model transmits HAR data to edge-computable nodes, deploys analytical models on edge servers for remote training, and returns results to wearable devices for processing. Then, we introduced an initial convolution method to preprocess large amounts of training data more effectively. To this end, an attention mechanism was integrated into the network structure to enhance the analysis of confusing data and improve the accuracy of action classification. Our results demonstrated that the proposed approach achieved an average accuracy of 85.4% on the 200 difficult-to-identify HAR data, which outperforms the Recurrent Neural Network (RNN) method’s accuracy of 77.1%. The experimental results showcase the efficacy of the proposed method and offer valuable insights for the future application of HAR.
Online cross-layer knowledge distillation on graph neural networks with deep supervision

Graph neural networks (GNNs) have become one of the most popular research topics in both academia and industry communities for their strong ability in handling irregular graph data. However, large-scale datasets are posing great challenges for deploying GNNs in edge devices with limited resources and model compression techniques have drawn considerable research attention. Existing model compression techniques such as knowledge distillation mainly focus on convolutional neural networks. Only limited attempts have been made recently for distilling knowledge from GNNs in an offline manner. As the performance of the teacher model does not necessarily improve as the number of layers increases in GNNs, selecting an appropriate teacher model will require substantial efforts. To address these challenges, we propose a novel online knowledge distillation framework called Alignahead++ in this paper. Alignahead++ transfers structure and feature information in a student layer to the previous layer of another simultaneously trained student model in an alternating training procedure. Meanwhile, to avoid over-smoothing problem in GNNs, deep supervision is employed in Alignahead++ by adding an auxiliary classifier in each intermediate layer to prevent the collapse of the node feature embeddings. Experimental results on four datasets including PPI, Cora, PubMed and CiteSeer demonstrate that the student performance is consistently boosted in our collaborative training framework without the supervision of a pre-trained teacher model and its effectiveness can generally be improved by increasing the number of students.
VPN Protection with QKD-Derived Keys Using Standard Interfaces

Quantum Key Distribution is a mechanism for obtaining a symmetric key that has been proved to be completely secure against quantum attacks. However, QKD technology is highly dependent on the low level implementation of the vendor. This work proposes the use of QKD-derived key material using high level standard interfaces to transparently protect services based on extending the protection of Virtual Private Networks to possible attacks based on quantum computing.
Analysis of CVSS Vulnerability Base Scores in the Context of Exploits’ Availability

Common Vulnerability Scoring System (CVSS) is a well-established standard for an evaluation of vulnerability criticality in Information and Communication Technology (ICT) infrastructure. In this paper, a particular attention is given to selected aspects of the temporal component of the CVSS 3.x vector. An analysis was performed aimed at relating the information provided by the basic and temporal components of the CVSS 3.x vector using a public vulnerability database of known vulnerabilities, National Vulnerability Database (NVD) created and maintained by the National Institute of Standards and Technology (NIST), and two available publicly exploit databases: Exploit Database and Attacker KB. Histograms were derived from the information available in the databases using python scripts. The results obtained show that some numerical values of base scores obtained applying CVSS v3.x are overrepresented when compared with the respective numbers of available exploits.