Abstract--Smart grids are exposed to passive eavesdropping, where attackers listen silently to communication links. Although no data is actively altered, such reconnaissance can reveal grid topology, consumption patterns, and operational behavior, creating a gateway to more severe targeted attacks. Detecting this threat is difficult because the signals it produces are faint, short-lived, and often disappear when traffic is examined by a single node or along a single timeline. This paper introduces a graph-centric, multimodal detector that fuses physical-layer (Channel State Information (CSI), Signal-to-Noise Ratio (SNR)) and behavioral (latency, Packet Error Rate (PER), event context) indicators over ego-centric star subgraphs and short temporal windows to detect passive attacks. T o capture stealthy perturbations, a two-stage encoder is introduced: graph convolution aggregates spatial context across ego-centric star subgraphs, while a bidirectional GRU models short-term temporal dependencies. The encoder transforms heterogeneous features into a unified spatio-temporal representation suitable for classification. Training occurs in a federated learning setup under FedProx, improving robustness to heterogeneous local raw data and contributing to the trustworthiness of decentralized training; raw measurements remain on client devices. The model achieves a testing accuracy of 98.32% per-timestep (F1 The results demonstrate that combining spatial and temporal context enables reliable detection of stealthy reconnaissance while maintaining low false-positive rates, making the approach suitable for non-IID federated smart-grid deployments. Smart grids [1] define new energy systems constructed on the notion of bidirectional communication between consumers and utilities. They enable the management of real-time data across distributed nodes. However, this open communication exposes the grid to significant risks of passive attacks, which pose a threat to privacy, trust, and stability [2].

Network intrusion detection, a well-explored cybersecurity field, has predominantly relied on supervised learning algorithms in the past two decades. However, their limitations in detecting only known anomalies prompt the exploration of alternative approaches. Motivated by the success of self-supervised learning in computer vision, there is a rising interest in adapting this paradigm for network intrusion detection. While prior research mainly delved into contrastive self-supervised methods, the efficacy of non-contrastive methods, in conjunction with encoder architectures serving as the representation learning backbone and augmentation strategies that determine what is learned, remains unclear for effective attack detection. This paper compares the performance of five non-contrastive self-supervised learning methods using three encoder architectures and six augmentation strategies. Ninety experiments are systematically conducted on two network intrusion detection datasets, UNSW-NB15 and 5G-NIDD. For each self-supervised model, the combination of encoder architecture and augmentation method yielding the highest average precision, recall, F1-score, and AUCROC is reported.

Consider a standard recommendation/retrieval problem where given a query, the goal is to retrieve the most relevant items. Inductive matrix completion (IMC) method is a standard approach for this problem where the given query as well as the items are embedded in a common low-dimensional space. The inner product between a query embedding and an item embedding reflects relevance of the (query, item) pair. Non-linear IMC (NIMC) uses non-linear networks to embed the query as well as items, and is known to be highly effective for a variety of tasks, such as video recommendations for users, semantic web search, etc. Despite its wide usage, existing literature lacks rigorous understanding of NIMC models.

Neural Information Processing Systems http://nips.cc/

The success of modern deep learning has been driven by large labeled datasets. Unfortunately, obtaining labeled data at scale is expensive and slow.

Finite-State Transducers (FST) are a standard tool for modeling paired input-output sequences and are used in numerous applications, ranging from computational biology to natural language processing. Recently Balle et al. [4] presented a spectral algorithm for learning FST from samples of aligned input-output sequences. In this paper we address the more realistic, yet challenging setting where the alignments are unknown to the learning algorithm. We frame FST learning as finding a low rank Hankel matrix satisfying constraints derived from observable statistics. Under this formulation, we provide identifiability results for FST distributions. Then, following previous work on rank minimization, we propose a regularized convex relaxation of this objective which is based on minimizing a nuclear norm penalty subject to linear constraints and can be solved efficiently.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper provides a generic way to perform parameter tuning of a given training algorithm in a deferentially private manner. Given a set of examples (for training and validation), a set of model parameters, a training algorithm, and a performance measure, the proposed procedure outputs a deferentially private hypothesis with respect to the prescribed privacy parameter. The basis behind the procedure is the definition of (\beta_1, \beta_2, \delta)-stability; which describes the stability of the performance with respect to change in the training set and the validation set. The procedure basically follows the exponential mechanism, and the utility bound is also provided.

Differential privacy is a cryptographically motivated definition of privacy which has gained considerable attention in the algorithms, machine-learning and data-mining communities. While there has been an explosion of work on differentially private machine learning algorithms, a major barrier to achieving end-to-end differential privacy in practical machine learning applications is the lack of an effective procedure for differentially private parameter tuning, or, determining the parameter value, such as a bin size in a histogram, or a regularization parameter, that is suitable for a particular application. In this paper, we introduce a generic validation procedure for differentially private machine learning algorithms that apply when a certain stability condition holds on the training algorithm and the validation performance metric. The training data size and the privacy budget used for training in our procedure is independent of the number of parameter values searched over. We apply our generic procedure to two fundamental tasks in statistics and machine-learning - training a regularized linear classifier and building a histogram density estimator that result in end-to-end differentially private solutions for these problems.

We study and provide instance-optimal algorithms in differential privacy by extending and approximating the inverse sensitivity mechanism.