Privacy Dynamics: Learning from the Wisdom of Groups
Lead Research Organisation:
Imperial College London
Department Name: Computing
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Publications
Alrajeh D
(2015)
Automated support for diagnosis and repair
in Communications of the ACM
Alrajeh, D
(2016)
Risk-Driven Revision of Requirements Models in Software Engineering
Alrajeh, D
(2016)
Logic-based Learning in Software Engineering
Athakravi D
(2015)
Inductive Logic Programming
Calikli G
(2016)
Privacy dynamics
Koschate M
(2021)
ASIA: Automated Social Identity Assessment using linguistic style
in Behavior Research Methods
Lavygina A
(2015)
Security and Trust Management
LAW M
(2016)
Iterative Learning of Answer Set Programs from Context Dependent Examples
in Theory and Practice of Logic Programming
LAW M
(2015)
Learning weak constraints in answer set programming
in Theory and Practice of Logic Programming
Rafiq, Yasmin
(2017)
Learning to Share: Engineering Adaptive Decision-Support for Online Social Networks
Smith J
(2014)
When did your smartphone bother you last?
Smith J
(2014)
Learning to recognise disruptive smartphone notifications
Turliuc C
(2016)
Probabilistic abductive logic programming using Dirichlet priors
in International Journal of Approximate Reasoning
| Description | The research conducted in this project made various research discoveries. A first major contribution is the development of new learning algorithms that are able to extract declarative knowledge from observations about human behaviours. the algorithms have increased expressivity than state-of-the-art systems and they are now de fact systems for learning declarative ASP programs. We have applied these systems to the context of privacy dynamics and have been able to show that it is indeed possible to learn privacy policies and privacy conflicts related to social identity conflicts. These results are currently been explored in the real setting of Facebook and other social networks. The second outcomes is the development of a probabilities learning approach as initially proposed. We are currently using this system for predicting level of sensitivity of pieces of information and use this in a framework for on-line learning of social benefits and privacy risk. These results also find immediate application in the context of social networks. A third contribution has been on the application of more conventional ML solutions to the classification of user behaviour on mobile phone with respect to their privacy awareness behaviour. |
| Exploitation Route | The learning systems developed in this project are general purpose systems. They can be applied to a large variety of problems and they are not specific to the task of learning privacy behaviours. This is one of the advantage of knowledge-driven machine learning versus our conventional machine learning. We envisage that these systems will be used to tackle a variety of learning tasks where the objective is to extract knowledge that can be expressed in English from observations. Current planned pathway to impact is the integration of these algorithms into the IBM Watson cognitive system in order to augment its cognitive capability. Other related and promising area of impacts are in healthcare where learning of hypothesis (diagnosis) is a key task of doctors. |
| Sectors | Communities and Social Services/Policy,Digital/Communication/Information Technologies (including Software),Energy,Healthcare,Transport,Other |
| URL | http://www9.open.ac.uk/PrivacyDynamics/ |
| Description | The findings provided by this award are mainly in the area of knowledge-absed learning and various applications. One of the applications is in the area of legal reasoning. The outcomes of learning algorithms of legal arguments has been considered by legal experts in Japan. The work was indeed conducted in collaboration with researchers at the NII in Japan, who have joint expertise in computer science and law. We hope that this initial successful discussions with law experts will in the future lead to further impact of the outcomes in society |
| First Year Of Impact | 2015 |
| Sector | Government, Democracy and Justice,Other |
| Impact Types | Policy & public services |
| Description | SAUSE |
| Amount | £1,330,879 (GBP) |
| Funding ID | EP/R013144/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 04/2018 |
| End | 03/2023 |
| Description | STRETCH |
| Amount | £1,049,532 (GBP) |
| Funding ID | EP/P01013X/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 04/2017 |
| End | 03/2020 |
| Description | Collaboration with University of York |
| Organisation | University of York |
| Department | Centre for Reviews and Dissemination (CRD) |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Application of probabilistic markov decision process to social networks. |
| Collaborator Contribution | Use of online learning approach to social network |
| Impact | No output yet. |
| Start Year | 2016 |
| Description | Memorandum of understanding with NII |
| Organisation | National Institute of Informatics (NII) |
| Country | Japan |
| Sector | Public |
| PI Contribution | Applied inductive learning algorithms develop during the EPSRC project to the context of legal reasoning. The collaboration is with Prof. Ken Satoh at the NII who is a computer scientist expert in knowledge representation of legal reasoning. |
| Collaborator Contribution | professor Ken Satoh provided us with legal case studies to study and to use in our learning approach, as well an contributed to the writing up of a paper. |
| Impact | Two papers: 1) Duangtida Athakravi, Dalal Alrajeh, Krysia Broda, Alessandra Russo, Ken Satoh: Inductive Learning Using Constraint-Driven Bias. ILP 2014: 16-32 2) Duangtida Athakravi, Ken Satoh, Mark Law, Krysia Broda, Alessandra Russo: Automated Inference of Rules with Exception from Past Legal Cases Using ASP. LPNMR 2015: 83-96. Legal reasoning Inductive learning |
| Start Year | 2014 |
| Description | Research collaboration with Miriam Koschate-Reis, University of Exeter, UK |
| Organisation | University of Exeter |
| Department | School of Psychology |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Worked together on applying machine learning to two real social network datasets to perform social identity detection. |
| Collaborator Contribution | They provided the social science input and the datasets. |
| Impact | It is multidisciplinary as Miriam is a Lecturer in Lecturer in Social and Organisational Psychology. We are currently writing a paper and an EPSRC reseat proposal. No completed outputs yet. |
| Start Year | 2014 |
| Title | ILASP |
| Description | Development of a state of the art learning system. Approach and method have been recognised as student IP by the college. |
| IP Reference | |
| Protection | Copyrighted (e.g. software) |
| Year Protection Granted | 2017 |
| Licensed | Yes |
| Impact | Formally licensed to college and myself on non-commercial basis. |
| Title | Learning system |
| Description | State of the art ILASP system has been development during the life of this project and the executable is publicly available on https://www.doc.ic.ac.uk/~ml1909/ILASP/. |
| Type Of Technology | Software |
| Year Produced | 2016 |
| Impact | Currently under discussion the possibility of an agreement with IBM for using this learning approach to develop cognitive system in health care. |