Abstract

Tornado Cash is a decentralized application (dApp) that runs on Ethereum Virtual Machine (EVM) compatible networks to enhance users’ privacy in terms of user transaction history over the blockchain. This dApp achieves its goal by enabling users to deposit currencies into designated pools and subsequently withdraw them, severing the link between depositor and withdrawer addresses. At deposit time, Tornado Cash communicates to users the level of privacy they will benefit from (anonymity set) by depositing currencies into one of its pools. Existing analyses have indicated discrepancies between the claimed anonymity set and the actual level of privacy provided, primarily attributed to users’ incorrect utilization of the dApp. This paper explores the road towards a new way to challenge the dApp’s proposed anonymity set by examining wallet fingerprints, a factor not directly related to user behavior within the application. The findings of this research shed light on the potential for creating links between clusters of users in TC according to the new proposed approach and raise a privacy concern within the Ethereum network, resulting in 13203 transactions, over 66948, linkable to the wallet used to initialize them.

Abstract

Moving Target Defense (MTD) has emerged as a proactive and dynamic framework to counteract evolving cyber threats. Traditional MTD approaches often rely on assumptions about the attacker’s knowledge and behavior. However, real-world scenarios are inherently more complex, with adaptive attackers and limited prior knowledge of their payoffs and intentions. This paper introduces a novel approach to MTD using a Markov Decision Process (MDP) model that does not rely on predefined attacker payoffs. Our framework integrates the attacker’s real-time responses into the defender’s MDP using a dynamic Bayesian Network. By employing a factored MDP model, we provide a comprehensive and realistic representation of the system and its dependencies. We also incorporate incremental updates to an attack response predictor as new data emerges, ensuring an adaptive and robust defense mechanism. Additionally, we consider the costs of switching configurations in MTD, integrating them into the reward structure to balance execution and defense costs. We first highlight the challenges of the problem through a theoretical negative result on regret. However, our empirical evaluations, conducted in network and web application settings, demonstrate the framework’s effectiveness in environments characterized by high uncertainty and dynamically changing attack surfaces.

Abstract

Text driven diffusion models have shown remarkable capabilities in editing images. However, when editing 3D scenes, existing works mostly rely on training a NeRF for 3D editing. Recent NeRF editing methods leverages edit operations by deploying 2D diffusion models and project these edits into 3D space. They require strong positional priors alongside text prompt to identify the edit location. These methods are operational on small 3D scenes and are more generalized to particular scene. They require training for each specific edit and cannot be exploited in real-time edits. To address these limitations, we propose a novel method, FreeEdit, to make edits in training free manner using mesh representations as a substitute for NeRF. Training-free methods are now a possibility because of the advances in foundation model’s space. We leverage these models to bring a training-free alternative and introduce solutions for insertion, replacement and deletion. We consider insertion, replacement and deletion as basic blocks for performing intricate edits with certain combinations of these operations. Given a text prompt and a 3D scene, our model is capable of identifying what object should be inserted/replaced or deleted and location where edit should be performed. We also introduce a novel algorithm as part of FreeEdit to find the optimal location on grounding object for placement. We evaluate our model by comparing it with baseline models on a wide range of scenes using quantitative and qualitative metrics and showcase the merits of our method with respect to others.

Abstract

No Results Found

Abstract

This paper considers the contextual multi-armed bandit (CMAB) problem with fairness and privacy guarantees in a federated environment. We consider merit-based exposure as the desired fair outcome, which provides exposure to each action in proportion to the reward associated. We model the algorithm's effectiveness using fairness regret, which captures the difference between fair optimal policy and the policy output by the algorithm. Applying fair CMAB algorithm to each agent individually leads to fairness regret linear in the number of agents. We propose that collaborative -- federated learning can be more effective and provide the algorithm Fed-FairX-LinUCB that also ensures differential privacy. The primary challenge in extending the existing privacy framework is designing the communication protocol for communicating required information across agents. A naive protocol can either lead to weaker privacy guarantees or higher regret. We design a novel communication protocol that allows for (i) Sub-linear theoretical bounds on fairness regret for Fed-FairX-LinUCB and comparable bounds for the private counterpart, Priv-FairX-LinUCB (relative to single-agent learning), (ii) Effective use of privacy budget in Priv-FairX-LinUCB. We demonstrate the efficacy of our proposed algorithm with extensive simulations-based experiments. We show that both Fed-FairX-LinUCB and Priv-FairX-LinUCB achieve near-optimal fairness regret.

Abstract

Restless multi-armed bandits (RMABs) generalize the multi-armed bandits where each arm exhibits Markovian behavior and transitions according to their transition dynamics. Solutions to RMAB exist for both offline and online cases. However, they do not consider the distribution of pulls among the arms. Studies have shown that optimal policies lead to unfairness, where some arms are not exposed enough. Existing works in fairness in RMABs focus heavily on the offline case, which diminishes their application in real-world scenarios where the environment is largely unknown. In the online scenario, we propose the first fair RMAB framework, where each arm receives pulls in proportion to its merit. We define the merit of an arm as a function of its stationary reward distribution. We prove that our algorithm achieves sublinear fairness regret in the single pull case , with being the total number of episodes. Empirically, we show that our algorithm performs well in the multi-pull scenario as well.

Abstract

Dysarthria due to Amyotrophic Lateral Sclerosis (ALS) affects the acoustic characteristics of different speech sounds. The effects intensify with increasing severity leading to the collapse of the acoustic space of the affected individuals. With an aim to characterize such changes in the acoustic space, this paper studies the variations in band-specific and full-band spectral properties of 4 sustained vowels (/a/, /i/, /o/, /u/) and 3 sustained fricatives (/s/, /sh/, /f/) at different dysarthria severity levels. Effect of dysarthria on spectral features of these phonemes are not well explored. Statistical comparison of these features among different severities for the phonemes considered and among different vowels/fricatives for every severity level using speech data from 119 ALS and 40 healthy subjects indicate the followings. Though all band-specific and full-band features of the three fricatives and most of those features for the four vowels become statistically similar at high severity levels, certain features remain distinguishable. Spectral differences in 0-2 kHz band between /a/ and the other vowels and in the 2-6 kHz band between /a/ and /o/, /u/ persist through all severity levels. Moreover, properties of /f/ remain mostly unchanged with increasing dysarthria severity levels.

Abstract

Machine Learning models increasingly face data integrity challenges due to the use of large-scale training datasets drawn from the Internet. We study what model developers can do if they detect that some data was manipulated or incorrect. Such manipulated data can cause adverse effects including vulnerability to backdoored samples, systemic biases, and reduced accuracy on certain input domains. Realistically, all manipulated training samples cannot be identified, and only a small, representative subset of the affected data can be flagged. We formalize ``Corrective Machine Unlearning'' as the problem of mitigating the impact of data affected by unknown manipulations on a trained model, only having identified a subset of the corrupted data. We demonstrate that the problem of corrective unlearning has significantly different requirements from traditional privacy-oriented unlearning. We find most existing unlearning methods, including retraining-from-scratch without the deletion set, require most of the manipulated data to be identified for effective corrective unlearning. However, one approach, Selective Synaptic Dampening, achieves limited success, unlearning adverse effects with just a small portion of the manipulated samples in our setting, which shows encouraging signs for future progress. We hope our work spurs research towards developing better methods for corrective unlearning and offers practitioners a new strategy to handle data integrity challenges arising from web-scale training.

Abstract

Stress has a significant mental health problem of the 21st century. The number of people suffering from stress is increasing rapidly. Thus, easy-to-use, inexpensive, and accurate biomarkers are needed to detect stress during its inception. Early detection of stress-related diseases allows people to access health-care services and leads to the development of new therapies. Thus, for the early detection of stress, a biomarker would be beneficial. We aim to find if there are significant differences between stressed and non-stressed groups of participants and which brain region gets impacted during stress. We conducted experiments to acquire EEG signals to identify the most significant brain region that gets affected due to stress. This research investigates if Higuchi Fractal Dimension (HFD) extracted from EEG signals can act as potential stress biomarkers. A gamified mobile application, known as Color Word and Memory Test (CWMT), was developed, inspired by the well-known Stroop Test, to elicit mental stress at two different difficulty levels. A MUSE headband with four EEG sensors (TP9, AF7, AF8, TP10) was used to collect the EEG signals at the two difficulty levels. The Higuchi Fractal Dimension (HFD) was extracted from EEG signals acquired from 32 participants while they were exposed to stress stimuli (using the proposed CWMT application) and we then performed three experimental analyses, i.e., Analysis I, II and III. Analysis I was performed on all the 32 participants performing low and high difficulty tasks on CWMT application. Analysis II was performed on sub-groups of participants. These sub-groups were made based on score obtained by participants while using CWMT application. Analysis II aimed to find the most significantly impacted brain region during stress. For Analysis III, we used the same sub-groups as Analysis II and it aimed to identify any differences between the left and right hemispheres during stress. We conducted the statistical analysis, and p-values were calculated between the two groups (non-stressed and stressed) to detect EEG channels and brain frequency significantly associated with stress. We performed three experimental analyses (Analysis I and II are intra-hemisphere, and analysis III is inter-hemisphere). In Analysis I, we inferred that beta and alpha frequencies from the AF8 region of the brain are affected during stress. In Analysis II, we inferred that beta waves from the AF8 region are a characteristic indicator of stress. In Analysis III, we identified significant differences between the left and right parts of the brain during stress. We found a significant difference (p<0.05) between HFD value in stress and non-stressed groups in the AF8 region. Our results indicated that stressed patients have a significantly higher value of HFD in the frontal areas.

Abstract

A number of anthropogenic and natural disasters that have impacted the fragile ecosystems of the Baratang group of islands in the Andaman and Nicobar archipelago, resulting in the degradation and depletion of their natural environment and biodiversity, have been identified. It is found that, in the early 20th century, the resources of the archipelago, especially Baratang Island, were exploited for infrastructure and plantations, resulting in a decrease in forest cover and an increase in anthropogenically transformed landscapes. However, the most recent available information on land use (LU) and land cover is only from 2013. This study aims to understand the dynamics of LU and land cover change in the Baratang Island group using multitemporal satellite data for 1976, 1993, 2004, 2014, and 2023. Using visual interpretation techniques, spatial and temporal change maps were created to represent the different LU and land cover features in the study area. An analysis of these maps revealed an interesting trend: there is an increase in forest area with a simultaneous decrease in settlements and agricultural land, which contradicts the findings of previous studies. This work has identified several factors contributing to this trend, namely natural regeneration, a reduction in logging and human activities, government policies and laws, and the implementation of conservation measures by the forest department. However, the evidence on mangrove decline attributed to both human activities and natural disasters is consistent with the results of other studies. This study emphasizes the importance of regularly monitoring the island’s landscape dynamics to understand changes in LU and land cover and to take appropriate conservation measures.