Jon Crowcroft

Bio: Jon Crowcroft is an academic researcher from University of Cambridge. The author has contributed to research in topics: The Internet & Multicast. The author has an hindex of 87, co-authored 672 publications receiving 38848 citations. Previous affiliations of Jon Crowcroft include Memorial University of Newfoundland & Information Technology University.

Privacy-Preserving Machine Learning Based Data Analytics on Edge Devices

Abstract: Emerging Machine Learning (ML) techniques, such as Deep Neural Network, are widely used in today's applications and services. However, with social awareness of privacy and personal data rapidly rising, it becomes a pressing and challenging societal issue to both keep personal data private and benefit from the data analytics power of ML techniques at the same time. In this paper, we argue that to avoid those costs, reduce latency in data processing, and minimise the raw data revealed to service providers, many future AI and ML services could be deployed on users' devices at the Internet edge rather than putting everything on the cloud. Moving ML-based data analytics from cloud to edge devices brings a series of challenges. We make three contributions in this paper. First, besides the widely discussed resource limitation on edge devices, we further identify two other challenges that are not yet recognised in existing literature: lack of suitable models for users, and difficulties in deploying services for users. Second, we present preliminary work of the first systematic solution, i.e. Zoo, to fully support the construction, composing, and deployment of ML models on edge and local devices. Third, in the deployment example, ML service are proved to be easy to compose and deploy with Zoo. Evaluation shows its superior performance compared with state-of-art deep learning platforms and Google ML services.

PiCasso: A lightweight edge computing platform

Abstract: Recent trends show that deploying low cost devices with lightweight virtualisation services is an attractive alternative for supporting the computational requirements at the network edge. Examples include inherently supporting the computational needs for local applications like smart cities and applications with stringent Quality of Service (QoS) requirements which are naturally hard to satisfy by traditional cloud infrastructures or supporting multi-access edge computing requirements of network in the box type solutions. The implementation of such a platform demands precise knowledge of several key system parameters, including the load that a service can tolerate and the number of service instances that a device can host. In this paper, we introduce PiCasso, a platform for lightweight service orchestration at the edges, and discuss the benchmarking results aimed at identifying the critical parameters that PiCasso needs to take into consideration.

Facebook and Privacy: The Balancing Act of Personality, Gender, and Relationship Currency

Abstract: Social media profiles are telling examples of the everyday need for disclosure and concealment. The balance between concealment and disclosure varies across individuals, and personality traits might partly explain this variability. Experimental findings on the relationship between information disclosure and personality have been so far inconsistent. We thus study this relationship anew with 1,313 Facebook users in the United States using two personality tests: the big five personality test and the self-monitoring test. We model the process of information disclosure in a principled way using Item Response Theory and correlate the resulting user disclosure scores with personality traits. We find a correlation with the trait of Openness and observe gender effects, in that, men and women share equal amount of private information, but men tend to make it more publicly available, well beyond their social circles. Interestingly, geographic (e.g., residence, hometown) and work-related information is used as relationship currency, in that, it is selectively shared with social contacts and is rarely shared with the Facebook community at large.

Y-Comm: A Global Architecture for Heterogeneous Networking

Abstract: In the near future mobile devices with several interfaces will become commonplace. Most of the peripheral networks using the Internet will therefore employ wireless technology. To provide support for these devices, this paper proposes a new framework which encompasses the functions of both peripheral and core networks. The framework is called Y-Comm and is defined in a layered manner like the OSI model.

EpiMap: Towards quantifying contact networks for understanding epidemiology in developing countries

Abstract: We describe the EpiMap project, together with the FluPhone project where we developed the basic technology for EpiMap. In FluPhone, human contact data is collected using mobile phones to record information such as locality and user symptoms for flu or cold. Delay tolerant opportunistic networks were used as a basis for communication. We are extending the technology used in FluPhone to gather information on human interactions within rural communities of developing countries. The collected information will be used to develop improved mathematical models for the spread of infectious diseases such as measles, tuberculosis and pneumococcal diseases. Survey study will aid the understanding of the living conditions in these villages. We introduce the EpiMap vision for a system of opportunistic networks combined with satellite communication, designed to face the challenges posed by weak electricity and communication infrastructures in rural regions of developing countries in Asia, Africa and South America. We aim to use a delay-tolerant small satellite for data transfer between developing countries and Europe ore North America. Data collected through EpiMap can be used to help design more efficient vaccination strategies and equitable control programmes.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Community detection in graphs

Abstract: The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e. g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.