Message Space Research Articles

A framework for the emergence and analysis of language in social learning agents

Neural systems have evolved not only to solve environmental challenges through internal representations but also, under social constraints, to communicate these to conspecifics. In this work, we aim to understand the structure of these internal representations and how they may be optimized to transmit pertinent information from one individual to another. Thus, we build on previous teacher-student communication protocols to analyze the formation of individual and shared abstractions and their impact on task performance. We use reinforcement learning in grid-world mazes where a teacher network passes a message to a student to improve task performance. This framework allows us to relate environmental variables with individual and shared representations. We compress high-dimensional task information within a low-dimensional representational space to mimic natural language features. In coherence with previous results, we find that providing teacher information to the student leads to a higher task completion rate and an ability to generalize tasks it has not seen before. Further, optimizing message content to maximize student reward improves information encoding, suggesting that an accurate representation in the space of messages requires bi-directional input. These results highlight the role of language as a common representation among agents and its implications on generalization capabilities.

Towards Practical Transciphering for FHE with Setup Independent of the Plaintext Space

Fully Homomorphic Encryption (FHE) is a powerful tool to achieve non-interactive privacy preserving protocols with optimal computation/communication complexity. However, the main disadvantage is that the actual communication cost (bandwidth) is high due to the large size of FHE ciphertexts. As a solution, a technique called transciphering (also known as Hybrid Homomorphic Encryption) was introduced to achieve almost optimal bandwidth for such protocols. However, all existing works require clients to fix a precision for the messages or a mathematical structure for the message space beforehand. It results in unwanted constraints on the plaintext size or underlying structure of FHE based applications. In this article, we introduce a new approach for transciphering which does not require fixed message precision decided by the client, for the first time. In more detail, a client uses any kind of FHE-friendly symmetric cipher for { 0 , 1 } to send its input data encrypted bit-by-bit, then the server can choose a precision p depending on the application and homomorphically transforms the encrypted bits into FHE ciphertexts encrypting integers in ℤ p . To illustrate our new technique, we evaluate a transciphering using FiLIP cipher and adapt the most practical homomorphic evaluation technique [CCS'22] to keep the practical latency. As a result, our proof-of-concept implementation for p from 2 2 to 2 8 takes only from 13 ms to 137 ms.

New tag-based signatures and their applications on linearly homomorphic signatures

Tag-based signature schemes can be efficiently converted into digital signature schemes using a generic transformation. However, there is no signature scheme that admits k>1 fold tag-collisions in the lattice environment as pointed by Ducas and Micciancio (2014). This work answers this problem in the stateful case. We use homomorphic hash functions and hash functions of tags to construct a SIS-based stateful tag-based signature (STS) scheme that admits k>1 fold tag-collisions. Messages are encoded prior to the signing procedure such that any k sequentially signed messages with the same tag form a basis for a vector subspace. The security analysis adopts a new abstraction called vector-space oriented partition. With the same technique, two STS schemes based on the CDH and the RSA assumptions, respectively, are proposed.As an application of our STS schemes, we show that those having field (or quasi-field) as message space can be converted into linearly homomorphic signature (LHS) schemes. Therefore, we immediately obtain CDH/RSA-based LHS scheme in the standard model under the same weaker assumption. Our LHS schemes can be viewed as “removing the restriction on the homomorphic property” from the related STS schemes. They have similar public key and signature sizes as the existing counterparts.

Communication between unbiased agents

We examine a version of the model of Crawford and Sobel (1982) in which agents are not biased, but their preferences are not necessarily smooth. In this situation, we show that communication converges to full information transmission as the number of messages used for communication increases if and only if the sender and the receiver have the same local relative preferences for avoiding small “upward” or “downward” mistakes. When these conditions fail, either an arbitrarily small bias or an arbitrarily small noise in the observation of the state may make communication very coarse in all equilibria, even when the message space is infinite. Hence, contrary to what was previously thought, continuity of preferences and close alignment between the sender's and receiver's ideal actions do not guarantee the existence of equilibria with precise information transmission.

An Efficient Dual-Stage Compression Model for Maritime Safety Information Based on BeiDou Short-Message Communication

In the context of utilizing BeiDou short-message communication (SMC) for transmitting maritime safety information, challenges arise regarding information redundancy and limited message length. To address these issues, compressing the data content of SMC becomes essential. This paper proposes a dual-stage compression model based on Beidou SMC for compressing maritime safety information, aiming to achieve efficient compression and reduce information redundancy. In the first stage, a binary encoding method (MBE) specifically designed for maritime safety information is proposed to optimize the byte space of the short messages, ensuring the accuracy, integrity, and reliability of the information. In the second stage, we propose a data compression algorithm called XH based on a hash dictionary, which efficiently compresses maritime safety information and reduces information redundancy. Different maritime data have corresponding structures and compositions, which can have a significant impact on the evaluation of compression algorithms. We create a database considering six categories of objects: waves, sea ice, tsunamis, storms, weather, and navigation warnings. Experimental results demonstrate that the proposed model achieves significant compression efficiency and performance on the maritime safety data set, outperforming other benchmark algorithms.

Bandwidth-Efficient Zero-Knowledge Proofs For Threshold ECDSA

Abstract In most threshold Elliptic Curve Digital Signature Algorithm (ECDSA) signatures using additively homomorphic encryption, the zero-knowledge (ZK) proofs related to the ciphertext or the message space are the bottleneck in terms of bandwidth as well as computation time. In this paper, we propose a compact ZK proof for relations related to the Castagnos–Laguillaumie (CL) encryption, which is 33% shorter and 29% faster than the existing work in PKC 2021. We also give new ZK proofs for relations related to homomorphic operations over the CL ciphertext. These new ZK proofs are useful to construct a bandwidth-efficient universal composable-secure threshold ECDSA without compromising the proactive security and the non-interactivity. In particular, we lowered the communication and computation cost of the key refresh algorithm in the Paillier-based counterpart from $O(n^3)$ to $O(n^2)$. Considering a 5-signer setting, the bandwidth is better than the Paillier-based counterpart for up to 99, 95 and 35% for key generation, key refreshment and pre-signing, respectively.

DPGNN: Dual-perception graph neural network for representation learning

Graph neural networks (GNNs) have drawn increasing attention in recent years and achieved remarkable performance in many graph-based tasks, especially in semi-supervised learning on graphs. However, most existing GNNs are based on the message-passing paradigm to iteratively aggregate neighborhood information in a single topology space. Despite their success, the expressive power of GNNs is limited by some drawbacks, such as inflexibility of message source expansion, negligence of node-level message output discrepancy, and restriction of single message space. To address these drawbacks, we present a novel message-passing paradigm, based on the properties of multi-step message source, node-specific message output, and multi-space message interaction. To verify its validity, we instantiate the new message-passing paradigm as a Dual-Perception Graph Neural Network (DPGNN), which applies a node-to-step attention mechanism to aggregate node-specific multi-step neighborhood information adaptively. Our proposed DPGNN can capture the structural neighborhood information and the feature-related information simultaneously for graph representation learning. Experimental results on six benchmark datasets with different topological structures demonstrate that our method outperforms the latest state-of-the-art models, which proves the superiority and versatility of our method. To our knowledge, we are the first to consider node-specific message passing in the GNNs.

On the primitivity of the AES-128 key-schedule

The key-scheduling algorithm in the AES is the component responsible for selecting from the master key the sequence of round keys to be xor-ed to the partially encrypted state at each iteration. We consider here the group [Formula: see text] generated by the action of the AES-128 key-scheduling operation, and we prove that the smallest group containing [Formula: see text] and all the translations of the message space is primitive. As a consequence, we obtain that no linear partition of the message space can be invariant under its action.

Non-commutative Ring Learning with Errors from Cyclic Algebras

The Learning with Errors (LWE) problem is the fundamental backbone of modern lattice-based cryptography, allowing one to establish cryptography on the hardness of well-studied computational problems. However, schemes based on LWE are often impractical, so Ring LWE was introduced as a form of ‘structured’ LWE, trading off a hard to quantify loss of security for an increase in efficiency by working over a well-chosen ring. Another popular variant, Module LWE, generalizes this exchange by implementing a module structure over a ring. In this work, we introduce a novel variant of LWE over cyclic algebras (CLWE) to replicate the addition of the ring structure taking LWE to Ring LWE by adding cyclic structure to Module LWE. We show that the security reductions expected for an LWE problem hold, namely a reduction from certain structured lattice problems to the hardness of the decision variant of the CLWE problem (under the condition of constant rank d). As a contribution of theoretic interest, we view CLWE as the first variant of Ring LWE which supports non-commutative multiplication operations. This ring structure compares favorably with Module LWE, and naturally allows a larger message space for error correction coding.

Algorithms for Persuasion with Limited Communication

The Bayesian persuasion paradigm of strategic communication models interaction between a privately informed sender and an ignorant but rational receiver. The goal is typically to design a (near-)optimal communication (or signaling) scheme for the sender. It enables the sender to disclose information to the receiver in a way as to incentivize her to take an action that is preferred by the sender. Finding the optimal signaling scheme is known to be computationally difficult in general. This hardness is further exacerbated when the message space is constrained, leading to NP-hardness of approximating the optimal sender utility within any constant factor. In this paper, we show that in several natural and prominent cases the optimization problem is tractable even when the message space is limited. In particular, we study signaling under a symmetry or an independence assumption on the distribution of utility values for the actions. For symmetric distributions, we provide a novel characterization of the optimal signaling scheme. It results in a polynomial-time algorithm to compute an optimal scheme for many compactly represented symmetric distributions. In the independent case, we design a constant-factor approximation algorithm, which stands in marked contrast to the hardness of approximation in the general case.

A model of competitive signaling with rich message spaces

A model of competitive signaling with rich message spaces

BASIC PRINCIPLES FOR CONSTRUCTING MIXING FUNCTIONS BASED ON THE SIMPLEST LINEAR AND NONLINEAR MAPPINGS

This article is dedicated to exploring the possibility of using the simplest principles of nonlinear discrete dynamic systems theory in computer cryptography, which are characterized by their chaotic behavior. The main problem of using chaotic systems in computer calculations is that the number of possible states in a computer is finite. Therefore, computer models of chaos are only an approximation of the true chaotic behavior, and each trajectory of the approximated system is periodic. From a mathematical point of view, encryption in information systems involves transforming the space of finite messages, which is similar to the phase space in the theory of dynamical systems. The mixing function specifies such encryption. The main requirements for the mixing function are the absence of collisions, i.e., bijectivity of the mapping, good diffusion properties, and, in addition, the inverse transformation should not be more complicated than the direct one. The article demonstrates that it is possible to utilize the diffusion properties of nonlinear dynamical systems in spaces with a finite number of states by using the simplest nonlinear mapping, Tent. To enhance the diffusion properties, a superposition of the nonlinear Tent map and the linear permutation map (in the more general case of the Hill map) was used. The main advantages of the constructed functions are their simplicity of implementation, speed of calculations in mixing problems, and strong cryptographic persistence. Correlation analysis, sensitivity analysis, and analysis of the lengths of cycle periods that divide the space into non-overlapping subsets have been conducted for these functions. As a result, the expected good diffusion properties of these mixing functions are confirmed. The possibility of applying these functions to image encryption is demonstrated.

A Novel Hybrid Encryption Method Based on Honey Encryption and Advanced DNA Encoding Scheme in Key Generation

Nowadays, increased information capacity and transmission processes make information security a difficult problem. As a result, most researchers employ encryption and decryption algorithms to enhance information security domains. As it progresses, new encryption methods are being used for information security. In this paper, a hybrid encryption algorithm that combines the honey encryption algorithm and an advanced DNA encoding scheme in key generation is presented. Deoxyribonucleic Acid (DNA) achieves maximal protection and powerful security with high capacity and low modification rate, it is currently being investigated as a potential carrier for information security. Honey Encryption (HE) is an important encryption method for security systems and can strongly prevent brute force attacks. However, the traditional honeyword encryption has a message space limitation problem in the message distribution process. Therefore, we use an improved honey encryption algorithm in our proposed system. By combining the benefits of the DNA-based encoding algorithm with the improved Honey encryption algorithm, a new hybrid method is created in the proposed system. In this paper, five different lookup tables are created in the DNA encoding scheme in key generation. The improved Honey encryption algorithm based on the DNA encoding scheme in key generation is discussed in detail. The passwords are generated as the keys by using the DNA methods based on five different lookup tables, and the disease names are the input messages that are encoded by using the honey encryption process. This hybrid method can reduce the storage overhead problem in the DNA method by applying the five different lookup tables and can reduce time complexity in the existing honey encryption process.

A subspace regenerating code with minimum decoding bandwidth

Hollmann built a theoretical framework for subspace regenerating storage code. In this framework, a storage node is equipped with a constant dimension subspace chosen from an overall message space F q m ${\bm{F}}_{\bm{q}}^{\bm{m}}$ . The projection of user message on the subspace is stored on the corresponding node. A subspace code needs to fulfil decoding and repairing functionality. The framework stipulates the property for subspace storage code, but a simple implementable construction is still missing. To decrease the complexity of code design as well as encoding and decoding, this article proposed a subspace code implementation over F2 with the parameters of ( m , m , 2 , ⌈ m 2 ⌉ , r , β ) $({\bm{m}},{\bm{m}},2,\lceil {\frac{{\bm{m}}}{2}} \rceil ,{\bm{\ r}},{\bm{\beta }})$ . It is proved theoretically the code is characterized by minimum decoding bandwidth, minimum storage, and exact repair. Still, the code is explicit, feasible and lightweight. A series of examples are given to elucidate the encoding, decoding and repairing procedures, which show in cases of m ≤ 5, the code could achieve high repair efficiency by combining with network coding.

Cryptanalysis of FRS obfuscation based on the CLT13 multilinear map

The authors present a classical polynomial-time attack against the branching program obfuscator of Fernando–Rasmussen–Sahai (for short FRS, Asiacrypt’17) (with one zerotest parameter), which is robust against all known classical cryptanalyses on obfuscators when instantiated with the CLT13 multilinear map. The first step is to recover a plaintext modulus of the CLT13 multilinear map. To achieve the goal, the Coron and Notarnicola (Asiacrypt’19) algorithm is applied. However, because of parameter issues, the algorithm cannot be used directly. In order to detour the issue, the authors convert an FRS obfuscator into a new programme containing a small message space. Through the conversion, the authors obtain two zerotest parameters and encodings of zero except for two non-zero slots. Then, they are used to mitigate parameter constraints of the message space recovering algorithm. Then, a cryptanalysis of the FRS obfuscation based on the recovered message space is proposed. The authors show that there exist two functionally equivalent programmes such that their obfuscated programmes are computationally distinguishable. Thus, the FRS scheme does not satisfy the desired security without any additional constraints.

Engineering Social Learning: Information Design of Time-Locked Sales Campaigns for Online Platforms

Many online platforms offer time-locked sales campaigns, whereby products are sold at fixed prices for prespecified lengths of time. Platforms often display some information about previous customers’ purchase decisions during campaigns. Using a dynamic Bayesian persuasion framework, we study how a revenue-maximizing platform should optimize its information policy for such a setting. We reformulate the platform’s problem equivalently by reducing the dimensionality of its message space and proprietary history. Specifically, three messages suffice: a neutral recommendation that induces a customer to make her purchase decision according to her private signal about the product and a positive (respectively (resp.), negative) recommendation that induces her to purchase (resp., not purchase) by ignoring her signal. The platform’s proprietary history can be represented by the net purchase position, a single-dimensional summary statistic that computes the cumulative difference between purchases and nonpurchases made by customers having received the neutral recommendation. Subsequently, we establish structural properties of the optimal policy and uncover the platform’s fundamental trade-off: long-term information (and revenue) generation versus short-term revenue extraction. Further, we propose and optimize over a class of heuristic policies. The optimal heuristic policy provides only neutral recommendations up to a cutoff customer and provides only positive or negative recommendations afterward, with the recommendation being positive if and only if the net purchase position after the cutoff customer exceeds a threshold. This policy is easy to implement and numerically shown to perform well. Finally, we demonstrate the generality of our methodology and the robustness of our findings by relaxing some informational assumptions. This paper was accepted by Gabriel Weintraub, revenue management and market analytics.

Achieving Efficient and Privacy-Preserving Multi-Domain Big Data Deduplication in Cloud

Secure data deduplication, as it can eliminate redundancies over encrypted data, has been widely developed in cloud storage to reduce storage space and communication overheads. Among them, the convergent encryption has been extensively adopted. However, it is vulnerable to brute-force attacks that can determine which plaintext in a message space corresponds to a given ciphertext. Many existing schemes have to sacrifice efficiency to resist brute-force attacks, especially for cross-domain deduplication, which is inevitably contrary to practical applications. Moreover, few existing schemes consider protecting the message equality information (i.e., whether two different ciphertexts correspond to an identical plaintext). To address the above challenges, in this paper, we propose an efficient and privacy-preserving big data deduplication scheme for a two-level multi-domain architecture. Specifically, by generating a random tag and a constant number of random ciphertexts for each data, our scheme not only ensures data confidentiality under multi-domain deduplication but also resists brute-force attacks. By allowing only the agent and cloud service provider to perform intra-deduplication and inter-deduplication, respectively, our scheme can protect the message equality information from disclosure as much as possible. Detailed security analysis shows that our scheme achieves privacy-preservation for both data content and the message equality information and data integrity while resisting brute-force attacks. Furthermore, extensive simulations demonstrate that our scheme significantly outperforms the existing competing schemes, especially the computational cost and the time complexity of the duplicate search.

Noisy persuasion

We study the effect of noise due to exogenous information distortions in the context of Bayesian persuasion. We first characterize the optimal signal in the prosecutor-judge game from Kamenica and Gentzkow (2011) with a noisy and strongly symmetric communication channel and show that the sender's payoff increases in the number of messages. This implies that, with exogenous noise, the sender prefers to complicate communication. Then, we establish necessary and sufficient conditions for the sender's payoff to weakly increase in the Blackwell-informativeness of the noise channel when the message space and the channel are binary. The reason why a sender may benefit from additional noise is that a garbling may alter the noise structure. Subsequently, we provide sufficient conditions that extend this result to channels of arbitrary cardinality. Finally, we introduce a procedure of making a communication channel more complex and prove that increased complexity benefits the sender.

Mechanism Design for Demand Management in Energy Communities

We consider a demand management problem in an energy community, in which several users obtain energy from an external organization such as an energy company and pay for the energy according to pre-specified prices that consist of a time-dependent price per unit of energy as well as a separate price for peak demand. Since users’ utilities are their private information, which they may not be willing to share, a mediator, known as the planner, is introduced to help optimize the overall satisfaction of the community (total utility minus total payments) by mechanism design. A mechanism consists of a message space, a tax/subsidy, and an allocation function for each user. Each user reports a message chosen from her own message space, then receives some amount of energy determined by the allocation function, and pays the tax specified by the tax function. A desirable mechanism induces a game, the Nash equilibria (NE), of which results in an allocation that coincides with the optimal allocation for the community. As a starting point, we design a mechanism for the energy community with desirable properties such as full implementation, strong budget balance and individual rationality for both users and the planner. We then modify this baseline mechanism for communities where message exchanges are allowed only within neighborhoods, and consequently, the tax/subsidy and allocation functions of each user are only determined by the messages from their neighbors. All of the desirable properties of the baseline mechanism are preserved in the distributed mechanism. Finally, we present a learning algorithm for the baseline mechanism, based on projected gradient descent, that is guaranteed to converge to the NE of the induced game.

Private Stream Aggregation with Labels in the Standard Model

Abstract A private stream aggregation (PSA) scheme is a protocol of n clients and one aggregator. At every time step, the clients send an encrypted value to the (untrusted) aggregator, who is able to compute the sum of all client values, but cannot learn the values of individual clients. One possible application of PSA is privacy-preserving smart-metering, where a power supplier can learn the total power consumption, but not the consumption of individual households. We construct a simple PSA scheme that supports labels and which we prove to be secure in the standard model. Labels are useful to restrict the access of the aggregator, because it prevents the aggregator from combining ciphertexts with different labels (or from different time-steps) and thus avoids leaking information about values of individual clients. The scheme is based on key-homomorphic pseudorandom functions (PRFs) as the only primitive, supports a large message space, scales well for a large number of users and has small ciphertexts. We provide an implementation of the scheme with a lattice-based key-homomorphic PRF (secure in the ROM) and measure the performance of the implementation. Furthermore, we discuss practical issues such as how to avoid a trusted party during the setup and how to cope with clients joining or leaving the system.