Reviewerly/exHarmony huggingface dataset:
Unsafe Diffusion: On the Generation of Unsafe Images and Hateful Memes From Text-To-Image Models

State-of-the-art Text-to-Image models like Stable Diffusion and DALLE\cdot2 are revolutionizing how people generate visual content. At the same time, society has serious concerns about how adversaries can exploit such models to generate problematic or unsafe images. In this work, we focus on demystifying the generation of unsafe images and hateful memes from Text-to-Image models. We first construct a typology of unsafe images consisting of five categories (sexually explicit, violent, disturbing, hateful, and political). Then, we assess the proportion of unsafe images generated by four advanced Text-to-Image models using four prompt datasets. We find that Text-to-Image models can generate a substantial percentage of unsafe images; across four models and four prompt datasets, 14.56% of all generated images are unsafe. When comparing the four Text-to-Image models, we find different risk levels, with Stable Diffusion being the most prone to generating unsafe content (18.92% of all generated images are unsafe). Given Stable Diffusion's tendency to generate more unsafe content, we evaluate its potential to generate hateful meme variants if exploited by an adversary to attack a specific individual or community. We employ three image editing methods, DreamBooth, Textual Inversion, and SDEdit, which are supported by Stable Diffusion to generate variants. Our evaluation result shows that 24% of the generated images using DreamBooth are hateful meme variants that present the features of the original hateful meme and the target individual/community; these generated images are comparable to hateful meme variants collected from the real world. Overall, our results demonstrate that the danger of large-scale generation of unsafe images is imminent. We discuss several mitigating measures, such as curating training data, regulating prompts, and implementing safety filters, and encourage better safeguard tools to be developed to prevent unsafe generation.1 Our code is available at https://github.com/YitingQu/unsafe-diffusion.
Logical Magic State Preparation with Fidelity beyond the Distillation Threshold on a Superconducting Quantum Processor

Fault-tolerant quantum computing based on surface code has emerged as an attractive candidate for practical large-scale quantum computers to achieve robust noise resistance. To achieve universality, magic states preparation is a commonly approach for introducing non-Clifford gates. Here, we present a hardware-efficient and scalable protocol for arbitrary logical state preparation for the rotated surface code, and further experimentally implement it on the Zuchongzhi 2.1 superconducting quantum processor. An average of 0.8983±0.0002 logical fidelity at different logical states with distance three is achieved, taking into account both state preparation and measurement errors. In particular, the logical magic states |A^{π/4}⟩_{L}, |H⟩_{L}, and |T⟩_{L} are prepared nondestructively with logical fidelities of 0.8771±0.0009, 0.9090±0.0009, and 0.8890±0.0010, respectively, which are higher than the state distillation protocol threshold, 0.859 (for H-type magic state) and 0.827 (for T-type magic state). Our work provides a viable and efficient avenue for generating high-fidelity raw logical magic states, which is essential for realizing non-Clifford logical gates in the surface code.
Uncle Maker: (Time)Stamping Out The Competition in Ethereum

We present and analyze an attack on Ethereum 1's consensus mechanism, which allows miners to obtain higher mining rewards compared to their honest peers. This attack is novel in that it relies on manipulating block timestamps and the difficulty-adjustment algorithm (DAA) to give the miner an advantage whenever block races ensue. We call our attack Uncle Maker, as it induces a higher rate of uncle blocks. We describe several variants of the attack. Among these, one that is risk-free for miners.
Stealth Key Exchange and Confined Access to the Record Protocol Data in TLS 1.3

We show how to embed a covert key exchange sub protocol within a regular TLS 1.3 execution, generating a stealth key in addition to the regular session keys. The idea, which has appeared in the literature before, is to use the exchanged nonces to transport another key value. Our contribution is to give a rigorous model and analysis of the security of such embedded key exchanges, requiring that the stealth key remains secure even if the regular key is under adversarial control. Specifically for our stealth version of the TLS 1.3 protocol we show that this extra key is secure in this setting under the common assumptions about the TLS protocol.
Are we there yet? An Industrial Viewpoint on Provenance-based Endpoint Detection and Response Tools

Provenance-Based Endpoint Detection and Response (P-EDR) systems are deemed crucial for future Advanced Persistent Threats (APT) defenses. Despite the fact that numerous new techniques to improve P-EDR systems have been proposed in academia, it is still unclear whether the industry will adopt P-EDR systems and what improvements the industry desires for P-EDR systems. To this end, we conduct the first set of systematic studies on the effectiveness and the limitations of P-EDR systems. Our study consists of four components: a one-to-one interview, an online questionnaire study, a survey of the relevant literature, and a systematic measurement study. Our research indicates that all industry experts consider P-EDR systems to be more effective than conventional Endpoint Detection and Response (EDR) systems. However, industry experts are concerned about the operating cost of P-EDR systems. In addition, our research reveals three significant gaps between academia and industry (1) overlooking client-side overhead; (2) imbalancedalarm triage cost and interpretation cost; and (3) excessive server side memory consumption. This paper's findings provide objective data on the effectiveness of P-EDR systems and how much improvements are needed to adopt P-EDR systems in industry.
AIM: Symmetric Primitive for Shorter Signatures with Stronger Security

Post-quantum signature schemes based on the MPC-in-the-Head (MPCitH) paradigm are recently attracting significant attention as their security solely depends on the one-wayness of the underlying primitive, providing diversity for the hardness assumption in post-quantum cryptography. Recent MPCitH-friendly ciphers have been designed using simple algebraic S-boxes operating on a large field in order to improve the performance of the resulting signature schemes. Due to their simple algebraic structures, their security against algebraic attacks should be comprehensively studied.
Boosting the Performance of High-Assurance Cryptography: Parallel Execution and Optimizing Memory Access in Formally-Verified Line-Point Zero-Knowledge

Despite the notable advances in the development of high-assurance, verified implementations of cryptographic protocols, such implementations typically face significant performance overheads, particularly due to the penalties induced by formal verification and automated extraction of executable code. In this paper, we address some core performance challenges facing computer-aided cryptography by presenting a formal treatment for accelerating such verified implementations based on multiple generic optimizations covering parallelism and memory access. We illustrate our techniques for addressing such performance bottlenecks using the Line-Point Zero-Knowledge (LPZK) protocol as a case study. Our starting point is a new verified implementation of LPZK that we formalize and synthesize using EasyCrypt; our first implementation is developed to reduce the proof effort and without considering the performance of the extracted executable code. We then show how such (automatically) extracted code can be optimized in three different ways to obtain a 3000x speedup and thus matching the performance of the manual implementation of LPZK of lpzkv2.[13] We obtain such performance gains by first modifying the algorithmic specifications, then by adopting a provably secure parallel execution model, and finally by optimizing the memory access structures. All optimizations are first formally verified inside EasyCrypt, and then executable code is automatically synthesized from each step of the formalization. For each optimization, we analyze performance gains resulting from it and also address challenges facing the computer-aided security proofs thereof, and challenges facing automated synthesis of executable code with such an optimization.
Let's Go Eevee! A Friendly and Suitable Family of AEAD Modes for IoT-to-Cloud Secure Computation

IoT devices collect privacy-sensitive data, e.g., in smart grids or in medical devices, and send this data to cloud servers for further processing. In order to ensure confidentiality as well as authenticity of the sensor data in the untrusted cloud environment, we consider a transciphering scenario between embedded IoT devices and multiple cloud servers that perform secure multi-party computation (MPC). Concretely, the IoT devices encrypt their data with a lightweight symmetric cipher and send the ciphertext to the cloud servers. To obtain the secret shares of the cleartext message for further processing, the cloud servers engage in an MPC protocol to decrypt the ciphertext in a distributed manner. This way, the plaintext is never exposed to the individual servers.
Sentiment Analysis of Arabic Course Reviews of a Saudi University Using Support Vector Machine

This study presents the development of a sentimental analysis system for high education students using Arabic text. There is a gap in the literature concerning understanding the perceptions and opinions of students in Saudi Arabia Universities regarding their education beyond COVID-19. The proposed SVM Sentimental Analysis for Arabic Students’ Course Reviews (SVM-SAA-SCR) algorithm is a general framework that involves collecting student reviews, preprocessing them, and using a machine learning model to classify them as positive, negative, or neutral. The suggested technique for preprocessing and classifying reviews includes steps such as collecting data, removing irrelevant information, tokenizing, removing stop words, stemming or lemmatization, and using pre-trained sentiment analysis models. The classifier is trained using the SVM algorithm and performance is evaluated using metrics such as accuracy, precision, and recall. Fine-tuning is done by adjusting parameters such as kernel type and regularization strength to optimize performance. A real dataset provided by the deanship of quality at Prince Sattam bin Abdulaziz University (PSAU) is used and contains students’ opinions on various aspects of their education. We also compared our algorithm with CAMeLBERT, a state-of-the-art Dialectal Arabic model. Our findings show that while the CAMeLBERT model classified 70.48% of the reviews as positive, our algorithm classified 69.62% as positive which proves the efficiency of the suggested SVM-SAA-SCR. The results of the proposed model provide valuable insights into the challenges and obstacles faced by Arab Universities post-COVID-19 and can help to improve their educational experience.
Towards an ethical framework for evaluating paid translation crowdsourcing and its consequences

The emergence of new technological tools for translating online has potential wide-reaching impacts for translators and the industry. However, very little research has so far systematically analysed the ethical dimension of such practices. To this end, this paper proposes an ethical framework to evaluate the consequences of translation platforms that use paid crowdsourcing on translators and clients. Based on the values that inform a good life, the paper proposes the use of multiple ethical principles: beneficence and non-maleficence, autonomy, and justice, to consider the different outcomes of the use of crowdsourcing platforms. Applying this framework to paid crowdsourcing platforms, the findings suggest that the translators' well-being and respect for their autonomy are undermined by platforms in several ways, while clients stand to gain from their use.
Longitudinal Impact of Preference Biases on Recommender Systems’ Performance

Recommender systems are ubiquitous on various online platforms and provide significant value to the users in helping them find relevant content/items to consume. After item consumption, users can often provide feedback (i.e., their preference ratings for the item) to the system. Research studies have shown that recommender systems’ predictions, observed by users, can cause biases in users’ postconsumption preference ratings. Because these ratings are typically fed back to the system as training data for future predictions, this process is likely to influence the system’s performance over time. We use a simulation approach to investigate the longitudinal impact of preference biases on the dynamics of recommender systems’ performance. Our results reveal that preference biases significantly impair recommendation performance and users’ consumption outcomes, and larger biases cause disproportionately large negative effects. Additionally, less popular and less distinctive (in terms of their content) items are more susceptible to preference biases. Furthermore, considering the substantial impact of preference biases on recommendation performance, we examine the issue of debiasing user-submitted ratings. We find that relying solely on historical rating data is unlikely to be effective in debiasing; thus, we propose/evaluate new debiasing approaches that use additional relevant information that can be collected by recommendation platforms.
Small Stochastic Data Compactification Concept Justified in the Entropy Basis

Measurement is a typical way of gathering information about an investigated object, generalized by a finite set of characteristic parameters. The result of each iteration of the measurement is an instance of the class of the investigated object in the form of a set of values of characteristic parameters. An ordered set of instances forms a collection whose dimensionality for a real object is a factor that cannot be ignored. Managing the dimensionality of data collections, as well as classification, regression, and clustering, are fundamental problems for machine learning. Compactification is the approximation of the original data collection by an equivalent collection (with a reduced dimension of characteristic parameters) with the control of accompanying information capacity losses. Related to compactification is the data completeness verifying procedure, which is characteristic of the data reliability assessment. If there are stochastic parameters among the initial data collection characteristic parameters, the compactification procedure becomes more complicated. To take this into account, this study proposes a model of a structured collection of stochastic data defined in terms of relative entropy. The compactification of such a data model is formalized by an iterative procedure aimed at maximizing the relative entropy of sequential implementation of direct and reverse projections of data collections, taking into account the estimates of the probability distribution densities of their attributes. The procedure for approximating the relative entropy function of compactification to reduce the computational complexity of the latter is proposed. To qualitatively assess compactification this study undertakes a formal analysis that uses data collection information capacity and the absolute and relative share of information losses due to compaction as its metrics. Taking into account the semantic connection of compactification and completeness, the proposed metric is also relevant for the task of assessing data reliability. Testing the proposed compactification procedure proved both its stability and efficiency in comparison with previously used analogues, such as the principal component analysis method and the random projection method.
A Novel Analysis of Utility in Privacy Pipelines, Using Kronecker Products and Quantitative Information Flow

We combine Kronecker products, and quantitative information flow, to give a novel formal analysis for the fine-grained verification of utility in complex privacy pipelines. The combination explains a surprising anomaly in the behaviour of utility of privacy-preserving pipelines - that sometimes a reduction in privacy results also in a decrease in utility. We use the standard measure of utility for Bayesian analysis, introduced by Ghosh at al. [1], to produce tractable and rigorous proofs of the fine-grained statistical behaviour leading to the anomaly. More generally, we offer the prospect of formal-analysis tools for utility that complement extant formal analyses of privacy. We demonstrate our results on a number of common privacy-preserving designs.
ACABELLA: Automated (Crypt)analysis of Attribute-Based Encryption Leveraging Linear Algebra

Attribute-based encryption (ABE) is a popular type of public-key encryption that enforces access control cryptographically, and has spurred the proposal of many use cases. To satisfy the requirements of the setting, tailor-made schemes are often introduced. However, designing secure schemes---as well as verifying that they are secure---is notoriously hard. Several of these schemes have turned out to be broken, making them dangerous to deploy in practice.
Deciding Differential Privacy of Online Algorithms with Multiple Variables

We consider the problem of checking the differential privacy of online randomized algorithms that process a stream of inputs and produce outputs corresponding to each input. This paper generalizes an automaton model called DiP automata [10] to describe such algorithms by allowing multiple real-valued storage variables. A DiP automaton is a parametric automaton whose behavior depends on the privacy budget ∈. An automaton A will be said to be differentially private if, for some D, the automaton is D∈-differentially private for all values of ∈ > 0. We identify a precise characterization of the class of all differentially private DiP automata. We show that the problem of determining if a given DiP automaton belongs to this class is PSPACE-complete. Our PSPACE algorithm also computes a value for D when the given automaton is differentially private. The algorithm has been implemented, and experiments demonstrating its effectiveness are presented.
Batchman and Robin: Batched and Non-batched Branching for Interactive ZK

Vector Oblivious Linear Evaluation (VOLE) supports fast and scalable interactive Zero-Knowledge (ZK) proofs. Despite recent improvements to VOLE-based ZK, compiling proof statements to a control-flow oblivious form (e.g., a circuit) continues to lead to expensive proofs. One useful setting where this inefficiency stands out is when the statement is a disjunction of clauses \mathcalL _1 łor \cdots łor \mathcalL _B. Typically, ZK requires paying the price to handle all B branches. Prior works have shown how to avoid this price in communication, but not in computation.
ASMesh: Anonymous and Secure Messaging in Mesh Networks Using Stronger, Anonymous Double Ratchet

The majority of secure messengers have single, centralized service providers that relay ciphertexts between users to enable asynchronous communication. However, in some scenarios such as mass protests in censored networks, relying on a centralized provider is fatal. Mesh messengers attempt to solve this problem by building ad hoc networks in which user clients perform the ciphertext-relaying task. Yet, recent analyses of widely deployed mesh messengers discover severe security weaknesses (Albrecht et al. CT-RSA'21 & USENIX Security'22).
Verifiable Verification in Cryptographic Protocols

Common verification steps in cryptographic protocols, such as signature or message authentication code checks or the validation of elliptic curve points, are crucial for the overall security of the protocol. Yet implementation errors omitting these steps easily remain unnoticed, as often the protocol will function perfectly anyways. One of the most prominent examples is Apple's goto fail bug where the erroneous certificate verification skipped over several of the required steps, marking invalid certificates as correctly verified. This vulnerability went undetected for at least 17 months.
A blockchain-based information market to incentivise cooperation in swarms of self-interested robots

Robot swarms are generally considered to be composed of cooperative agents that, despite their limited individual capabilities, can perform difficult tasks by working together. However, in open swarms, where different robots can be added to the swarm by different parties with potentially competing interests, cooperation is but one of many strategies. We envision an information market where robots can buy and sell information through transactions stored on a distributed blockchain, and where cooperation is encouraged by the economy itself. As a proof of concept, we study a classical foraging task, where exchanging information with other robots is paramount to accomplish the task efficiently. We illustrate that even a single robot that lies to others-a so-called Byzantine robot-can heavily disrupt the swarm. Hence, we devise two protection mechanisms. Through an individual-level protection mechanism, robots are more sceptical about others' information and can detect and discard Byzantine information, at the cost of lower efficiency. Through a systemic protection mechanism based on economic rules regulating robot interactions, robots that sell honest information acquire over time more wealth than Byzantines selling false information. Our simulations show that a well-designed robot economy penalises misinformation spreading and protects the swarm from Byzantine behaviour. We believe economics-inspired swarm robotics is a promising research direction that exploits the timely opportunity for decentralised economies offered by blockchain technology.
Quantum neural networks for the discovery and implementation of quantum error-correcting codes

We implement and use quantum neural networks that exploit bit-flip quantum error-correcting codes that correct bit-flip errors in arbitrary logical qubit states. We introduce conjugate layer quantum autoencoders and use them in order to restore states impacted by amplitude damping through the utilization of an approximative four-qubit error-correcting codeword. Our specific implementation avoids barren plateaus of the cost function and improves the training time. Moreover, we propose a strategy that allows one to discover new encryption protocols tailored for specific quantum channels. This is exemplified by learning to generate logical qubits explicitly for the bit-flip channel. Our modified quantum neural networks consistently outperform the standard implementations across all tasks.
Forecasting of PV Plant Output Using Interpretable Temporal Fusion Transformer Model

The stochastic nature of solar energy generation poses a challenge for grid operators, especially with higher penetration of solar-based renewables in the grid. This paper proposes an attention-based temporal fusion transformer (TFT) model for short-term (an hour ahead) photovoltaic (PV) power forecasting using available geographic data such as solar irradiation, temperature, and statistical features extracted from historical PV data. TFT utilizes a self-attention layer for long-term dependencies where recurrent networks are used for local processing. The model selects relevant features through a series of gating layers to achieve high performance for multi-horizon forecasting. The temporal fusion transformer model also provides interpretable insights into the temporal dynamics of different features. A real-world PV dataset has been utilized to compare the model performance with some other state-of-the-art forecasting models.