Hamed Haddadi

Hamed Haddadi
Queen Mary, University of London | QMUL · School of Electronic Engineering and Computer Science

About

193
Publications
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6,106
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Publications

Publications (193)
Preprint
Privacy and security challenges in Machine Learning (ML) have become a critical topic to address, along with ML's pervasive development and the recent demonstration of large attack surfaces. As a mature system-oriented approach, confidential computing has been increasingly utilized in both academia and industry to improve privacy and security in va...
Preprint
Increasing use of our biometrics (e.g., fingerprints, faces, or voices) to unlock access to and interact with online services raises concerns about the trade-offs between convenience, privacy, and security. Service providers must authenticate their users, although individuals may wish to maintain privacy and limit the disclosure of sensitive attrib...
Article
Mobile networks and devices provide the users with ubiquitous connectivity, while many of their functionality and business models rely on data analysis and processing. In this context, Machine Learning (ML) plays a key role and has been successfully leveraged by the different actors in the mobile ecosystem (e.g., application and Operating System de...
Preprint
Full-text available
Machine Learning (ML) techniques have begun to dominate data analytics applications and services. Recommendation systems are a key component of online service providers. The financial industry has adopted ML to harness large volumes of data in areas such as fraud detection, risk-management, and compliance. Deep Learning is the technology behind voi...
Preprint
Full-text available
A core problem in the development and maintenance of crowd- sourced filter lists is that their maintainers cannot confidently predict whether (and where) a new filter list rule will break websites. This is a result of enormity of the Web, which prevents filter list authors from broadly understanding the impact of a new blocking rule before they shi...
Preprint
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? An affirmative answer to this question would have significant implications for a new generation of underwater sensing and monitoring applications for environmental monitoring, scientific ex...
Preprint
Full-text available
Advances in cloud computing have simplified the way that both software development and testing are performed. This is not true for battery testing for which state of the art test-beds simply consist of one phone attached to a power meter. These test-beds have limited resources, access, and are overall hard to maintain; for these reasons, they often...
Preprint
Full-text available
We identify a new class of side-channels in browsers that are not mitigated by current defenses. This class of side-channels, which we call "pool-party" attacks, allow sites to create covert channels by manipulating limited-but-unpartitioned resource pools in browsers. We identify pool-party attacks in all popular browsers, and show they are practi...
Preprint
Consumer Internet of Things (IoT) devices are increasingly common in everyday homes, from smart speakers to security cameras. Along with their benefits come potential privacy and security threats. To limit these threats we must implement solutions to filter IoT traffic at the edge. To this end the identification of the IoT device is the first natur...
Chapter
Full-text available
This paper outlines the IoT Databox model as an in principle means of making the Internet of Things (IoT) accountable to individuals. Accountability is key to building consumer trust and is mandated by the European Union’s General Data Protection Regulation (GDPR). We focus here on the external data subject accountability requirement specified by G...
Chapter
Full-text available
This volume brings together a collection of interdisciplinary works that are in various ways concerned to address the societal challenge to privacy and security occasioned by the Internet of Things (IoT). The chapters in this volume cover legal, social science, systems, and design research perspectives. Taken together they seek to enable the broade...
Article
Full-text available
Despite the prevalence of Internet of Things (IoT) devices, there is little information about the purpose and risks of the Internet traffic these devices generate, and consumers have limited options for controlling those risks. A key open question is whether one can mitigate these risks by automatically blocking some of the Internet connections fro...
Preprint
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. In many applications, such as smart homes with IoT devices, local data on clients are generated from different modalities such as sensory, visual, and audio...
Article
Motion sensors embedded in wearable and mobile devices allow for dynamic selection of sensor streams and sampling rates, enabling several applications, such as power management and data-sharing control. While deep neural networks (DNNs) achieve competitive accuracy in sensor data classification, DNN architectures generally process incoming data fro...
Preprint
Speech synthesis, voice cloning, and voice conversion techniques present severe privacy and security threats to users of voice user interfaces (VUIs). These techniques transform one or more elements of a speech signal, e.g., identity and emotion, while preserving linguistic information. Adversaries may use advanced transformation tools to trigger a...
Preprint
Full-text available
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. This requires robustly identifying devices so that appropriate network security policies can be applied. We address this challenge by exploring how to accurately identify IoT devices based on thei...
Preprint
Sharing deep neural networks' gradients instead of training data could facilitate data privacy in collaborative learning. In practice however, gradients can disclose both private latent attributes and original data. Mathematical metrics are needed to quantify both original and latent information leakages from gradients computed over the training da...
Preprint
Despite the prevalence of Internet of Things (IoT) devices, there is little information about the purpose and risks of the Internet traffic these devices generate, and consumers have limited options for controlling those risks. A key open question is whether one can mitigate these risks by automatically blocking some of the Internet connections fro...
Preprint
Full-text available
We propose and implement a Privacy-preserving Federated Learning (PPFL) framework for mobile systems to limit privacy leakages in federated learning. Leveraging the widespread presence of Trusted Execution Environments (TEEs) in high-end and mobile devices, we utilize TEEs on clients for local training, and on servers for secure aggregation, so tha...
Preprint
Voice assistive technologies have given rise to far-reaching privacy and security concerns. In this paper we investigate whether modular automatic speech recognition (ASR) can improve privacy in voice assistive systems by combining independently trained separation, recognition, and discretization modules to design configurable privacy-preserving AS...
Preprint
Full-text available
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. This requires robustly identifying devices so that appropriate network security policies can be applied. We address this challenge by exploring how to accurately identify IoT devices based on their...
Preprint
Voice user interfaces and digital assistants are rapidly entering our homes and becoming integrated with all our devices. These always-on services capture and transmit our audio data to powerful cloud services for further processing and subsequent actions. Our voices and raw audio signals collected through these devices contain a host of sensitive...
Preprint
The proliferation of IoT sensors and edge devices makes it possible to use deep learning models to recognise daily activities locally using in-home monitoring technologies. Recently, federated learning systems that use edge devices as clients to collect and utilise IoT sensory data for human activity recognition have been commonly used as a new way...
Preprint
Training a deep neural network (DNN) via federated learning allows participants to share model updates (gradients), instead of the data itself. However, recent studies show that unintended latent information (e.g. gender or race) carried by the gradients can be discovered by attackers, compromising the promised privacy guarantee of federated learni...
Article
Full-text available
Internet-connected voice-controlled speakers, also known as smart speakers , are increasingly popular due to their convenience for everyday tasks such as asking about the weather forecast or playing music. However, such convenience comes with privacy risks: smart speakers need to constantly listen in order to activate when the “wake word” is spoken...
Preprint
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. This is particularly important as the data transfer costs to the host system and dedicated machine learning accelerators, e.g., GPUs, can be more exp...
Preprint
Full-text available
Consumer Internet of Things (IoT) devices are extremely popular, providing users with rich and diverse functionalities, from voice assistants to home appliances. These functionalities often come with significant privacy and security risks, with notable recent large scale coordinated global attacks disrupting large service providers. Thus, an import...
Preprint
Current deep neural architectures for processing sensor data are mainly designed for data coming from a fixed set of sensors, with a fixed sampling rate. Changing the dimensions of the input data causes considerable accuracy loss, unnecessary computations, or application failures. To address this problem, we introduce a {\em dimension-adaptive pool...
Preprint
Voice User Interfaces (VUIs) are increasingly popular and built into smartphones, home assistants, and Internet of Things (IoT) devices. Despite offering an always-on convenient user experience, VUIs raise new security and privacy concerns for their users. In this paper, we focus on attribute inference attacks in the speech domain, demonstrating th...
Article
Full-text available
Conditional generative adversarial networks (CGANs) are a recent and popular method for generating samples from a probability distribution conditioned on latent information. The latent information often comes in the form of a discrete label from a small set. We propose a novel method for training CGANs which allows us to condition on a sequence of...
Article
Machine Learning as a Service (MLaaS) operators provide model training and prediction on the cloud. MLaaS applications often rely on centralised collection and aggregation of user data, which could lead to significant privacy concerns when dealing with sensitive personal data. To address this problem, we propose PrivEdge, a technique for privacy-pr...
Preprint
Full-text available
Machine Learning as a Service (MLaaS) operators provide model training and prediction on the cloud. MLaaS applications often rely on centralised collection and aggregation of user data, which could lead to significant privacy concerns when dealing with sensitive personal data. To address this problem, we propose PrivEdge, a technique for privacy-pr...
Preprint
Full-text available
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). Increasingly, edge devices (smartphones and consumer IoT devices) are equipped with pre-trained DNNs for a variety of applications. This trend comes w...
Preprint
The consumer Internet of Things (IoT) space has experienced a significant rise in popularity in the recent years. From smart speakers, to baby monitors, and smart kettles and TVs, these devices are increasingly found in households around the world while users may be unaware of the risks associated with owning these devices. Previous work showed tha...
Preprint
We are increasingly surrounded by applications, connected devices, services, and smart environments which require fine-grained access to various personal data. The inherent complexities of our personal and professional policies and preferences in interactions with these analytics services raise important challenges in privacy. Moreover, due to sens...
Article
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. To mitigate these threats, we propose mechanisms to transform sensor data before sharing them with applications running on users’ devices. These transformations aim at elimi...
Conference Paper
Full-text available
Recent advances in cloud computing have simplified the way that both software development and testing are performed. Unfortunately, this is not true for battery testing for which state of the art test-beds simply consist of one phone attached to a power meter. These test-beds have limited resources, access, and are overall hard to maintain; for the...
Preprint
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. To mitigate these threats, we propose mechanisms to transform sensor data before sharing them with applications running on users' devices. These transformations aim at elimi...
Conference Paper
Remembering our day-to-day social interactions is challenging even if you aren't a blue memory challenged fish. The ability to automatically detect and remember these types of interactions is not only beneficial for individuals interested in their behavior in crowded situations, but also of interest to those who analyze crowd behavior. Currently, d...
Conference Paper
Voice controlled devices and services are commonplace in consumer IoT. Cloud-based analysis services extract information from voice input using speech recognition techniques. Services providers can build detailed profiles of users' demographics, preferences and emotional states, etc., and may therefore significantly compromise privacy. To address t...
Conference Paper
There has been a growing interest in measuring and optimizing the power efficiency of mobile apps. Traditional power evaluations rely either on inaccurate software-based solutions or on ad-hoc testbeds composed of a power meter and a mobile device. This demonstration presents BatteryLab, our solution to share existing battery testing setups to buil...
Conference Paper
Full-text available
As more and more devices are connected to the Internet-of-Things, often made by non-specialist companies or short-lived startups, the likelihood that these devices will be hacked and used for nefarious activity online increases. We seek to support non-expert users in managing the network behaviour of their IoT devices, and assisting them in handlin...
Conference Paper
Full-text available
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. Based on the concept of generalization error, we propose a framework to measure the amount of sensitive i...
Conference Paper
Voice controlled devices and services have become very popular in the consumer IoT. Cloud-based speech analysis services extract information from voice inputs using speech recognition techniques. Services providers can thus build very accurate profiles of users' demographic categories, personal preferences, emotional states, etc., and may therefore...
Preprint
Full-text available
Recent advances in cloud computing have simplified the way that both software development and testing are performed. Unfortunately, this is not true for battery testing for which state of the art test-beds simply consist of one phone attached to a power meter. These test-beds have limited resources, access, and are overall hard to maintain; for the...
Preprint
Contextual bandit algorithms (CBAs) often rely on personal data to provide recommendations. This means that potentially sensitive data from past interactions are utilized to provide personalization to end-users. Using a local agent on the user's device protects the user's privacy, by keeping the data locally, however, the agent requires longer to p...
Preprint
Full-text available
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. Based on the concept of generalization error, we propose a framework to measure the amount of sensitive i...
Preprint
Conditional Generative Adversarial Networks (CGANs) are a recent and popular method for generating samples from a probability distribution conditioned on latent information. The latent information often comes in the form of a discrete label from a small set. We propose a novel method for training CGANs which allows us to condition on a sequence of...
Preprint
Full-text available
In this paper, we introduce Zest (REST over ZeroMQ), a middleware technology in support of an Internet of Things (IoT). Our work is influenced by the Constrained Application Protocol (CoAP) but emphasises systems that can support fine-grained access control to both resources and audit information, and can provide features such as asynchronous commu...
Preprint
Privacy-preserving data release is about disclosing information about useful data while retaining the privacy of sensitive data. Assuming that the sensitive data is threatened by a brute-force adversary, we define Guessing Leakage as a measure of privacy, based on the concept of guessing. After investigating the properties of this measure, we deriv...
Conference Paper
Full-text available
Occupancy modelling for efficient energy management of indoor spaces has gained significant recent attention. Unfortunately, many such models rely on copying sensor data to the cloud for third-party services to process, creating risks of privacy breach. Such matters have become particularly pertinent for companies handling data of EU citizens due t...
Preprint
Data from motion sensors such as accelerometers and gyroscopes embedded in our devices can reveal secondary undesired, private information about our activities. This information can be used for malicious purposes such as user identification by application developers. To address this problem, we propose a data transformation mechanism that enables a...