A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Proposes a secure communication method independent of eavesdropper's decoder.
We develop a secure aggregation protocol for federated learning that reduces communication and computation costs.
Federated Learning enables mobile devices to collaboratively learn a shared inference model while keeping all the training data on a user's device, decoupling the ability to do machine learning from the need to store the data in the cloud. Existing work on federated learning with limited communication demonstrates how …
The problem of secure friend discovery on a social network has long been proposed and studied. The requirement is that a pair of nodes can make befriending decisions with minimum information exposed to the other party. In this paper, we propose to use community detection to tackle the problem of secure friend discovery…
End-to-end learning of codes for secure BPSK communication in Gaussian wiretap channel.
We consider distributed on-device learning with limited communication and security requirements. We propose a new robust distributed optimization algorithm with efficient communication and attack tolerance. The proposed algorithm has provable convergence and robustness under non-IID settings. Empirical results show tha…
FastSecAgg improves federated learning security and efficiency.
Secure Aggregation protocols allow a collection of mutually distrust parties, each holding a private value, to collaboratively compute the sum of those values without revealing the values themselves. We consider training a deep neural network in the Federated Learning model, using distributed stochastic gradient descen…
This paper explores advancements in neural network communication for distributed settings.
We present cyber-security problems of high importance. We show that in order to solve these cyber-security problems, one must cope with certain machine learning challenges. We provide novel data sets representing the problems in order to enable the academic community to investigate the problems and suggest methods to c…
Paper proposes a secure protocol for federated learning.
Our work specifies the fundamental cost of using secure aggregation in federated learning.
As neural networks revolutionize many applications, significant privacy conflicts between model users and providers emerge. The cryptography community developed a variety of techniques for secure computation to address such privacy issues. As generic techniques for secure computation are typically prohibitively ineffec…
New methods reduce private federated learning communication automatically.
Deep learning is increasingly used as a building block of security systems. Unfortunately, neural networks are hard to interpret and typically opaque to the practitioner. The machine learning community has started to address this problem by developing methods for explaining the predictions of neural networks. While sev…
Federated learning promises to make machine learning feasible on distributed, private datasets by implementing gradient descent using secure aggregation methods. The idea is to compute a global weight update without revealing the contributions of individual users. Current practical protocols for secure aggregation work…
A system for federated learning with private data, adding discrete Gaussian noise and secure aggregation.
Proposes a method for private aggregation in heterogeneous federated learning.
FLFE improves machine learning by efficiently and securely transforming features.
A new method for federated learning aggregates data from multiple sites efficiently.
To accommodate heterogeneous tasks in Internet of Things (IoT), a new communication and computing paradigm termed mobile edge computing emerges that extends computing services from the cloud to edge, but at the same time exposes new challenges on security. The present paper studies online security-aware edge computing …
FTTQ optimizes quantized networks in federated learning, reducing communication costs.
The ever-growing big data and emerging artificial intelligence (AI) demand the use of machine learning (ML) and deep learning (DL) methods. Cybersecurity also benefits from ML and DL methods for various types of applications. These methods however are susceptible to security attacks. The adversaries can exploit the tra…
Since the inception of Deep Reinforcement Learning (DRL) algorithms, there has been a growing interest in both research and industrial communities in the promising potentials of this paradigm. The list of current and envisioned applications of deep RL ranges from autonomous navigation and robotics to control applicatio…
We introduce CheckNet, a method for secure inference with deep neural networks on untrusted devices. CheckNet is like a checksum for neural network inference: it verifies the integrity of the inference computation performed by untrusted devices to 1) ensure the inference has actually been performed, and 2) ensure the i…
Post-Quantum Secure Federated DeFi for Inclusive Banking
Paper proposes efficient privacy-preserving matrix encryption for secure collaborative learning against malicious adversaries.
Federated machine learning systems have been widely used to facilitate the joint data analytics across the distributed datasets owned by the different parties that do not trust each others. In this paper, we proposed a novel Gradient Boosting Machines (GBM) framework SecureGBM built-up with a multi-party computation mo…
This work develops secure distributed algorithms for machine learning to protect against data poisoning and network attacks.
Efficient algorithm reduces communication costs in sparse regression.
Data privacy and security becomes a major concern in building machine learning models from different data providers. Federated learning shows promise by leaving data at providers locally and exchanging encrypted information. This paper studies the vertical federated learning structure for logistic regression where the …
Platform uses queries to elicit investor preferences for portfolio trades, improving allocation efficiency.
Paper aims to bridge semantic gap between ML and InfoSec by labeling malware datasets with behavioral features.
FedElasticNet reduces communication costs and handles client drift in FL.
Paper uses GNNs to automatically detect botnets from network data.
Connected and autonomous vehicles (CAVs) will form the backbone of future next-generation intelligent transportation systems (ITS) providing travel comfort, road safety, along with a number of value-added services. Such a transformation---which will be fuelled by concomitant advances in technologies for machine learnin…
Quantum federated learning improves with non-IID data using one-shot communication.
Secure federated learning reduces privacy risks with differential privacy and secure multiparty computation.
Nowadays, privacy preserving machine learning has been drawing much attention in both industry and academy. Meanwhile, recommender systems have been extensively adopted by many commercial platforms (e.g. Amazon) and they are mainly built based on user-item interactions. Besides, social platforms (e.g. Facebook) have ri…
New spectral clustering method for graphs with uneven node degrees.
The arms race between attacks and defenses for machine learning models has come to a forefront in recent years, in both the security community and the privacy community. However, one big limitation of previous research is that the security domain and the privacy domain have typically been considered separately. It is t…
Paper proposes scalable privacy-preserving DNN for industrial applications.
Achieving international food security requires improved understanding of how international trade networks connect countries around the world through the import-export flows of food commodities. The properties of food trade networks are still poorly documented, especially from a multi-network perspective. In particular,…
PBM mechanism improves privacy and accuracy in federated learning.
Combining differential privacy and federated learning improves data security.
The paper addresses challenges in edge deep learning for IoT, proposing new directions.
New method recovers radar and communication signals from overlaid data.
Timely detection of Hardware Trojans (HTs) has become a major challenge for secure integrated circuits. We present a run-time methodology for HT detection that employs a multi-parameter statistical traffic modeling of the communication channel in a given System-on-Chip (SoC), named as SIMCom. The main idea is to model …