Search Results for author: Hamed Haddadi

Found 52 papers, 17 papers with code

Effective Abnormal Activity Detection on Multivariate Time Series Healthcare Data

no code implementations11 Sep 2023 Mengjia Niu, Yuchen Zhao, Hamed Haddadi

Multivariate time series (MTS) data collected from multiple sensors provide the potential for accurate abnormal activity detection in smart healthcare scenarios.

Action Detection Activity Detection +3

A Study on the Reliability of Automatic Dysarthric Speech Assessments

no code implementations7 Jun 2023 Xavier F. Cadet, Ranya Aloufi, Sara Ahmadi-Abhari, Hamed Haddadi

Automating dysarthria assessments offers the opportunity to develop effective, low-cost tools that address the current limitations of manual and subjective assessments.

Evaluating The Robustness of Self-Supervised Representations to Background/Foreground Removal

no code implementations2 Jun 2023 Xavier F. Cadet, Ranya Aloufi, Alain Miranville, Sara Ahmadi-Abhari, Hamed Haddadi

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.

Image Classification

Towards Machine Learning and Inference for Resource-constrained MCUs

no code implementations30 May 2023 Yushan Huang, Hamed Haddadi

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

no code implementations9 May 2023 Yacine Belal, Sonia Ben Mokhtar, Hamed Haddadi, Jaron Wang, Afra Mashhadi

Federated learning involves training statistical models over edge devices such as mobile phones such that the training data is kept local.

Community Detection Federated Learning +2

Analysing Fairness of Privacy-Utility Mobility Models

no code implementations10 Apr 2023 Yuting Zhan, Hamed Haddadi, Afra Mashhadi

Preserving the individuals' privacy in sharing spatial-temporal datasets is critical to prevent re-identification attacks based on unique trajectories.

Fairness Privacy Preserving +1

Information Theory Inspired Pattern Analysis for Time-series Data

1 code implementation22 Feb 2023 Yushan Huang, Yuchen Zhao, Alexander Capstick, Francesca Palermo, Hamed Haddadi, Payam Barnaghi

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.

Time Series Time Series Analysis

Using Entropy Measures for Monitoring the Evolution of Activity Patterns

no code implementations4 Oct 2022 Yushan Huang, Yuchen Zhao, Hamed Haddadi, Payam Barnaghi

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.

Time Series Analysis

Machine Learning with Confidential Computing: A Systematization of Knowledge

no code implementations22 Aug 2022 Fan Mo, Zahra Tarkhani, Hamed Haddadi

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

no code implementations9 Aug 2022 Yuting Zhan, Hamed Haddadi, Afra Mashhadi

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.

Privacy Preserving Representation Learning

FIB: A Method for Evaluation of Feature Impact Balance in Multi-Dimensional Data

no code implementations10 Jul 2022 Xavier F. Cadet, Sara Ahmadi-Abhari, Hamed Haddadi

Nevertheless, there is limited research investigating the impact of imbalance in the contribution of different features in an error vector.

Model Selection

Distributed data analytics

no code implementations26 Mar 2022 Richard Mortier, Hamed Haddadi, Sandra Servia, Liang Wang

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.

Cloud Computing Fraud Detection +2

Towards Battery-Free Machine Learning and Inference in Underwater Environments

no code implementations16 Feb 2022 Yuchen Zhao, Sayed Saad Afzal, Waleed Akbar, Osvy Rodriguez, Fan Mo, David Boyle, Fadel Adib, Hamed Haddadi

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?

BIG-bench Machine Learning

Privacy-Aware Human Mobility Prediction via Adversarial Networks

no code implementations19 Jan 2022 Yuting Zhan, Alex Kyllo, Afra Mashhadi, Hamed Haddadi

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.

Privacy Preserving Representation Learning

Rapid IoT Device Identification at the Edge

no code implementations26 Oct 2021 Oliver Thompson, Anna Maria Mandalari, Hamed Haddadi

Consumer Internet of Things (IoT) devices are increasingly common in everyday homes, from smart speakers to security cameras.

Multimodal Federated Learning on IoT Data

no code implementations10 Sep 2021 Yuchen Zhao, Payam Barnaghi, Hamed Haddadi

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.

Federated Learning

Revisiting IoT Device Identification

1 code implementation16 Jul 2021 Roman Kolcun, Diana Andreea Popescu, Vadim Safronov, Poonam Yadav, Anna Maria Mandalari, Richard Mortier, Hamed Haddadi

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

no code implementations28 May 2021 Fan Mo, Anastasia Borovykh, Mohammad Malekzadeh, Soteris Demetriou, Deniz Gündüz, Hamed Haddadi

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.

PPFL: Privacy-preserving Federated Learning with Trusted Execution Environments

1 code implementation29 Apr 2021 Fan Mo, Hamed Haddadi, Kleomenis Katevas, Eduard Marin, Diego Perino, Nicolas Kourtellis

We propose and implement a Privacy-preserving Federated Learning ($PPFL$) framework for mobile systems to limit privacy leakages in federated learning.

Federated Learning Privacy Preserving

Configurable Privacy-Preserving Automatic Speech Recognition

no code implementations1 Apr 2021 Ranya Aloufi, Hamed Haddadi, David Boyle

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

no code implementations17 Nov 2020 Roman Kolcun, Diana Andreea Popescu, Vadim Safronov, Poonam Yadav, Anna Maria Mandalari, Yiming Xie, Richard Mortier, Hamed Haddadi

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

no code implementations4 Nov 2020 Ranya Aloufi, Hamed Haddadi, David Boyle

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.

Knowledge Distillation Privacy Preserving +3

Semi-supervised Federated Learning for Activity Recognition

no code implementations2 Nov 2020 Yuchen Zhao, Hanyang Liu, Honglin Li, Payam Barnaghi, Hamed Haddadi

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.

Data Augmentation Federated Learning +1

Layer-wise Characterization of Latent Information Leakage in Federated Learning

no code implementations17 Oct 2020 Fan Mo, Anastasia Borovykh, Mohammad Malekzadeh, Hamed Haddadi, Soteris Demetriou

Training deep neural networks via federated learning allows clients to share, instead of the original data, only the model trained on their data.

Federated Learning

Running Neural Networks on the NIC

no code implementations4 Sep 2020 Giuseppe Siracusano, Salvator Galea, Davide Sanvito, Mohammad Malekzadeh, Hamed Haddadi, Gianni Antichi, Roberto Bifulco

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.

BIG-bench Machine Learning

DANA: Dimension-Adaptive Neural Architecture for Multivariate Sensor Data

2 code implementations5 Aug 2020 Mohammad Malekzadeh, Richard G. Clegg, Andrea Cavallaro, Hamed Haddadi

We introduce a dimension-adaptive pooling (DAP) layer that makes DNNs flexible and more robust to changes in sensor availability and in sampling rate.

Human Activity Recognition Imputation +1

Privacy-preserving Voice Analysis via Disentangled Representations

no code implementations29 Jul 2020 Ranya Aloufi, Hamed Haddadi, David Boyle

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.

Privacy Preserving Representation Learning +2

PrivEdge: From Local to Distributed Private Training and Prediction

1 code implementation12 Apr 2020 Ali Shahin Shamsabadi, Adria Gascon, Hamed Haddadi, Andrea Cavallaro

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.

Image Compression Privacy Preserving

DarkneTZ: Towards Model Privacy at the Edge using Trusted Execution Environments

2 code implementations12 Apr 2020 Fan Mo, Ali Shahin Shamsabadi, Kleomenis Katevas, Soteris Demetriou, Ilias Leontiadis, Andrea Cavallaro, Hamed Haddadi

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).

Image Classification

Towards Automatic Identification and Blocking of Non-Critical IoT Traffic Destinations

no code implementations16 Mar 2020 Anna Maria Mandalari, Roman Kolcun, Hamed Haddadi, Daniel J. Dubois, David Choffnes

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

2 code implementations14 Mar 2020 Kleomenis Katevas, Eugene Bagdasaryan, Jason Waterman, Mohamad Mounir Safadieh, Eleanor Birrell, Hamed Haddadi, Deborah Estrin

In this paper we present PoliFL, a decentralized, edge-based framework that supports heterogeneous privacy policies for federated learning.

Federated Learning Image Classification

Privacy and Utility Preserving Sensor-Data Transformations

1 code implementation14 Nov 2019 Mohammad Malekzadeh, Richard G. Clegg, Andrea Cavallaro, Hamed Haddadi

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.

Activity Recognition

Privacy-Preserving Bandits

1 code implementation10 Sep 2019 Mohammad Malekzadeh, Dimitrios Athanasakis, Hamed Haddadi, Benjamin Livshits

Contextual bandit algorithms~(CBAs) often rely on personal data to provide recommendations.

Multi-Label Classification Privacy Preserving

Emotionless: Privacy-Preserving Speech Analysis for Voice Assistants

1 code implementation9 Aug 2019 Ranya Aloufi, Hamed Haddadi, David Boyle

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.

Emotion Recognition Privacy Preserving +3

Towards Characterizing and Limiting Information Exposure in DNN Layers

no code implementations13 Jul 2019 Fan Mo, Ali Shahin Shamsabadi, Kleomenis Katevas, Andrea Cavallaro, Hamed Haddadi

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

1 code implementation21 Jun 2019 Edoardo Lisi, Mohammad Malekzadeh, Hamed Haddadi, F. Din-Houn Lau, Seth Flaxman

Realisations from this distribution can be used by the CGAN to generate "future" paintings.

Mobile Sensor Data Anonymization

1 code implementation26 Oct 2018 Mohammad Malekzadeh, Richard G. Clegg, Andrea Cavallaro, Hamed Haddadi

Motion sensors such as accelerometers and gyroscopes measure the instant acceleration and rotation of a device, in three dimensions.

Activity Recognition

Deep Learning in Mobile and Wireless Networking: A Survey

no code implementations12 Mar 2018 Chaoyun Zhang, Paul Patras, Hamed Haddadi

One potential solution is to resort to advanced machine learning techniques to help managing the rise in data volumes and algorithm-driven applications.

Link Prediction Management

Protecting Sensory Data against Sensitive Inferences

1 code implementation21 Feb 2018 Mohammad Malekzadeh, Richard G. Clegg, Andrea Cavallaro, Hamed Haddadi

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.

Activity Recognition Gender Classification +1

Distributed One-class Learning

no code implementations10 Feb 2018 Ali Shahin Shamsabadi, Hamed Haddadi, Andrea Cavallaro

A major advantage of the proposed filter over existing distributed learning approaches is that users cannot access, even indirectly, the parameters of other users.

Blocking One-class classifier

Deep Private-Feature Extraction

1 code implementation9 Feb 2018 Seyed Ali Osia, Ali Taheri, Ali Shahin Shamsabadi, Kleomenis Katevas, Hamed Haddadi, Hamid R. Rabiee

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

1 code implementation18 Oct 2017 Mohammad Malekzadeh, Richard G. Clegg, Hamed Haddadi

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.

Activity Recognition Privacy Preserving +2

Privacy-Preserving Deep Inference for Rich User Data on The Cloud

1 code implementation4 Oct 2017 Seyed Ali Osia, Ali Shahin Shamsabadi, Ali Taheri, Kleomenis Katevas, Hamid R. Rabiee, Nicholas D. Lane, Hamed Haddadi

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.

Privacy Preserving

A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics

1 code implementation8 Mar 2017 Seyed Ali Osia, Ali Shahin Shamsabadi, Sina Sajadmanesh, Ali Taheri, Kleomenis Katevas, Hamid R. Rabiee, Nicholas D. Lane, Hamed Haddadi

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

Kissing Cuisines: Exploring Worldwide Culinary Habits on the Web

no code implementations26 Oct 2016 Sina Sajadmanesh, Sina Jafarzadeh, Seyed Ali Osia, Hamid R. Rabiee, Hamed Haddadi, Yelena Mejova, Mirco Musolesi, Emiliano De Cristofaro, Gianluca Stringhini

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

no code implementations25 Feb 2016 Tiago O. Cunha, Ingmar Weber, Hamed Haddadi, Gisele L. Pappa

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

no code implementations20 Jan 2015 Hamed Haddadi, Heidi Howard, Amir Chaudhry, Jon Crowcroft, Anil Madhavapeddy, Richard Mortier

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

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