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Found 60 papers, 20 papers with code
Terracorder: Sense Long and Prosper
In-situ sensing devices need to be deployed in remote environments for long periods of time; minimizing their power consumption is vital for maximising both their operational lifetime and coverage.
P4: Towards private, personalized, and Peer-to-Peer learning
Personalized learning is a proposed approach to address the problem of data heterogeneity in collaborative machine learning.
Mitigating Hallucinations in Large Language Models via Self-Refinement-Enhanced Knowledge Retrieval
Large language models (LLMs) have demonstrated remarkable capabilities across various domains, although their susceptibility to hallucination poses significant challenges for their deployment in critical areas such as healthcare.
Towards Low-Energy Adaptive Personalization for Resource-Constrained Devices
For each type, we fine-tune different blocks of the model to achieve optimal performance with reduced energy costs.
MELTing point: Mobile Evaluation of Language Transformers
Transformers have revolutionized the machine learning landscape, gradually making their way into everyday tasks and equipping our computers with "sparks of intelligence".
MicroT: Low-Energy and Adaptive Models for MCUs
On MCUs, compared to the standard full model inference, MicroT can save up to about 29. 13% in energy consumption.
SunBlock: Cloudless Protection for IoT Systems
With an increasing number of Internet of Things (IoT) devices present in homes, there is a rise in the number of potential information leakage channels and their associated security threats and privacy risks.
Federated Learning for Clinical Structured Data: A Benchmark Comparison of Engineering and Statistical Approaches
Our goal was to bridge the gap by presenting the first comprehensive comparison of FL frameworks from both engineering and statistical domains.
Effective Abnormal Activity Detection on Multivariate Time Series Healthcare Data
Multivariate time series (MTS) data collected from multiple sensors provide the potential for accurate abnormal activity detection in smart healthcare scenarios.
A study on the impact of Self-Supervised Learning on automatic dysarthric speech assessment
We are the first to evaluate the representations from pre-trained state-of-the-art Self-Supervised models across three downstream tasks on dysarthric speech: disease classification, word recognition and intelligibility classification, and under three noise scenarios on the UA-Speech dataset.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Evaluating The Robustness of Self-Supervised Representations to Background/Foreground Removal
Despite impressive empirical advances of SSL in solving various tasks, the problem of understanding and characterizing SSL representations learned from input data remains relatively under-explored.
Towards Machine Learning and Inference for Resource-constrained MCUs
The results demonstrate that, our pipeline can achieve 97. 78% accuracy with 483. 82 KB Flash, 70. 32 KB RAM, 118 ms runtime, 4. 83 mW power, and 0. 57 mJ energy consumption on MCUs, reducing by 64. 17%, 12. 31%, 52. 42%, 63. 74%, and 82. 67%, compared to the baseline.
Survey of Federated Learning Models for Spatial-Temporal Mobility Applications
Federated learning involves training statistical models over edge devices such as mobile phones such that the training data is kept local.
Federated and distributed learning applications for electronic health records and structured medical data: A scoping review
Structured data, one of the most prevalent forms of clinical data, has experienced significant growth in volume concurrently, notably with the widespread adoption of electronic health records in clinical practice.
Analysing Fairness of Privacy-Utility Mobility Models
Preserving the individuals' privacy in sharing spatial-temporal datasets is critical to prevent re-identification attacks based on unique trajectories.
Information Theory Inspired Pattern Analysis for Time-series Data
For applications with stochastic state transitions, features are developed based on Shannon's entropy of Markov chains, entropy rates of Markov chains, entropy production of Markov chains, and von Neumann entropy of Markov chains.
PRISM: Privacy Preserving Healthcare Internet of Things Security Management
In this paper, we propose PRISM, an edge-based system for experimenting with in-home smart healthcare devices.
Using Entropy Measures for Monitoring the Evolution of Activity Patterns
The study uses Internet of Things (IoT) enabled solutions for continuous monitoring of in-home activity, sleep, and physiology to develop care and early intervention solutions to support people living with dementia (PLWD) in their own homes.
Machine Learning with Confidential Computing: A Systematization of Knowledge
Privacy and security challenges in Machine Learning (ML) have become increasingly severe, along with ML's pervasive development and the recent demonstration of large attack surfaces.
Privacy-Aware Adversarial Network in Human Mobility Prediction
As mobile devices and location-based services are increasingly developed in different smart city scenarios and applications, many unexpected privacy leakages have arisen due to geolocated data collection and sharing.
FIB: A Method for Evaluation of Feature Impact Balance in Multi-Dimensional Data
Nevertheless, there is limited research investigating the impact of imbalance in the contribution of different features in an error vector.
Distributed data analytics
A contrasting approach, distributed data analytics, where code and models for training and inference are distributed to the places where data is collected, has been boosted by two recent, ongoing developments: increased processing power and memory capacity available in user devices at the edge of the network, such as smartphones and home assistants; and increased sensitivity to the highly intrusive nature of many of these devices and services and the attendant demands for improved privacy.
Towards Battery-Free Machine Learning and Inference in Underwater Environments
This paper is motivated by a simple question: Can we design and build battery-free devices capable of machine learning and inference in underwater environments?
Privacy-Aware Human Mobility Prediction via Adversarial Networks
Our proposed architecture reports a Pareto Frontier analysis that enables the user to assess this trade-off as a function of Lagrangian loss weight parameters.
Rapid IoT Device Identification at the Edge
Consumer Internet of Things (IoT) devices are increasingly common in everyday homes, from smart speakers to security cameras.
Multimodal Federated Learning on IoT Data
Federated learning is proposed as an alternative to centralized machine learning since its client-server structure provides better privacy protection and scalability in real-world applications.
Revisiting IoT Device Identification
Internet-of-Things (IoT) devices are known to be the source of many security problems, and as such, they would greatly benefit from automated management.
Quantifying and Localizing Usable Information Leakage from Neural Network Gradients
Our proposed framework enables clients to localize and quantify the private information leakage in a layer-wise manner, and enables a better understanding of the sources of information leakage in collaborative learning, which can be used by future studies to benchmark new attacks and defense mechanisms.
Stronger Privacy for Federated Collaborative Filtering with Implicit Feedback
Recommender systems are commonly trained on centrally collected user interaction data like views or clicks.
PPFL: Privacy-preserving Federated Learning with Trusted Execution Environments
We propose and implement a Privacy-preserving Federated Learning ($PPFL$) framework for mobile systems to limit privacy leakages in federated learning.
Configurable Privacy-Preserving Automatic Speech Recognition
We show that overlapping speech inputs to ASR systems present further privacy concerns, and how these may be mitigated using speech separation and optimization techniques.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
The Case for Retraining of ML Models for IoT Device Identification at the Edge
We therefore evaluate our approach using hardware resources and data sources representative of those that would be available at the edge of the network, such as in an IoT deployment.
Paralinguistic Privacy Protection at the Edge
One approach to mitigate the risk of paralinguistic-based privacy breaches is to exploit a combination of cloud-based processing with privacy-preserving, on-device paralinguistic information learning and filtering before transmitting voice data.
Semi-supervised Federated Learning for Activity Recognition
In this paper, we propose an activity recognition system that uses semi-supervised federated learning, wherein clients conduct unsupervised learning on autoencoders with unlabelled local data to learn general representations, and a cloud server conducts supervised learning on an activity classifier with labelled data.
Layer-wise Characterization of Latent Information Leakage in Federated Learning
Training deep neural networks via federated learning allows clients to share, instead of the original data, only the model trained on their data.
Running Neural Networks on the NIC
In this paper we show that the data plane of commodity programmable (Network Interface Cards) NICs can run neural network inference tasks required by packet monitoring applications, with low overhead.
DANA: Dimension-Adaptive Neural Architecture for Multivariate Sensor Data
We introduce a dimension-adaptive pooling (DAP) layer that makes DNNs flexible and more robust to changes in sensor availability and in sampling rate.
Privacy-preserving Voice Analysis via Disentangled Representations
Our experimental evaluation over five datasets shows that the proposed framework can effectively defend against attribute inference attacks by reducing their success rates to approximately that of guessing at random, while maintaining accuracy in excess of 99% for the tasks of interest.
PrivEdge: From Local to Distributed Private Training and Prediction
To address this problem, we propose PrivEdge, a technique for privacy-preserving MLaaS that safeguards the privacy of users who provide their data for training, as well as users who use the prediction service.
DarkneTZ: Towards Model Privacy at the Edge using Trusted Execution Environments
We present DarkneTZ, a framework that uses an edge device's Trusted Execution Environment (TEE) in conjunction with model partitioning to limit the attack surface against Deep Neural Networks (DNNs).
Towards Automatic Identification and Blocking of Non-Critical IoT Traffic Destinations
Our initial results demonstrate that some IoT devices contact destinations that are not critical to their operation, and there is no impact on device functionality if these destinations are blocked.
Networking and Internet Architecture Cryptography and Security
Policy-Based Federated Learning
In this paper we present PoliFL, a decentralized, edge-based framework that supports heterogeneous privacy policies for federated learning.
Privacy and Utility Preserving Sensor-Data Transformations
Sensitive inferences and user re-identification are major threats to privacy when raw sensor data from wearable or portable devices are shared with cloud-assisted applications.
Privacy-Preserving Bandits
Contextual bandit algorithms~(CBAs) often rely on personal data to provide recommendations.
Emotionless: Privacy-Preserving Speech Analysis for Voice Assistants
The voice signal is a rich resource that discloses several possible states of a speaker, such as emotional state, confidence and stress levels, physical condition, age, gender, and personal traits.
Towards Characterizing and Limiting Information Exposure in DNN Layers
Pre-trained Deep Neural Network (DNN) models are increasingly used in smartphones and other user devices to enable prediction services, leading to potential disclosures of (sensitive) information from training data captured inside these models.
Modeling and Forecasting Art Movements with CGANs
Realisations from this distribution can be used by the CGAN to generate "future" paintings.
Mobile Sensor Data Anonymization
Motion sensors such as accelerometers and gyroscopes measure the instant acceleration and rotation of a device, in three dimensions.
Finding Dory in the Crowd: Detecting Social Interactions using Multi-Modal Mobile Sensing
Remembering our day-to-day social interactions is challenging even if you aren't a blue memory challenged fish.
Deep Learning in Mobile and Wireless Networking: A Survey
One potential solution is to resort to advanced machine learning techniques to help managing the rise in data volumes and algorithm-driven applications.
Ranked #1 on
Link Prediction
on SINS
Protecting Sensory Data against Sensitive Inferences
Results show that the proposed framework maintains the usefulness of the transformed data for activity recognition, with an average loss of only around three percentage points, while reducing the possibility of gender classification to around 50\%, the target random guess, from more than 90\% when using raw sensor data.
Distributed One-class Learning
A major advantage of the proposed filter over existing distributed learning approaches is that users cannot access, even indirectly, the parameters of other users.
Deep Private-Feature Extraction
We present and evaluate Deep Private-Feature Extractor (DPFE), a deep model which is trained and evaluated based on information theoretic constraints.
Replacement AutoEncoder: A Privacy-Preserving Algorithm for Sensory Data Analysis
Though access to the sensory data is critical to the success of many beneficial applications such as health monitoring or activity recognition, a wide range of potentially sensitive information about the individuals can also be discovered through access to sensory data and this cannot easily be protected using traditional privacy approaches.
Privacy-Preserving Deep Inference for Rich User Data on The Cloud
Our evaluations show that by using certain kind of fine-tuning and embedding techniques and at a small processing costs, we can greatly reduce the level of information available to unintended tasks applied to the data feature on the cloud, and hence achieving the desired tradeoff between privacy and performance.
A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics
To this end, instead of performing the whole operation on the cloud, we let an IoT device to run the initial layers of the neural network, and then send the output to the cloud to feed the remaining layers and produce the final result.
Privacy-Preserving Personal Model Training
Many current Internet services rely on inferences from models trained on user data.
Kissing Cuisines: Exploring Worldwide Culinary Habits on the Web
In this paper, we present a large-scale study of recipes published on the web and their content, aiming to understand cuisines and culinary habits around the world.
The Effect of Social Feedback in a Reddit Weight Loss Community
It is generally accepted as common wisdom that receiving social feedback is helpful to (i) keep an individual engaged with a community and to (ii) facilitate an individual's positive behavior change.
Social and Information Networks
Personal Data: Thinking Inside the Box
We propose there is a need for a technical platform enabling people to engage with the collection, management and consumption of personal data; and that this platform should itself be personal, under the direct control of the individual whose data it holds.
Computers and Society
