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Department of Mathematical Sciences

Research Seminar Series

Applied Mathematics Seminars

Statistics Seminars: Towards Encrypted Inference for Arbitrary Models

Presented by Louis Aslett , Durham University

9 October 2017 14:00 in CM221

There has been substantial progress in development of statistical methods which are amenable to computation with modern cryptographic techniques, such as homomorphic encryption. This has enabled fitting and/or prediction of models in areas from classification and regression through to genome wide association studies. However, these are techniques devised to address specific models in specific settings, with the broader challenge of an approach to inference for arbitrary models and arbitrary data sets receiving less attention. This talk will discuss very recent results from ongoing work towards an approach which may allow theoretically arbitrary low dimensional models to be fitted fully encrypted, keeping the model and prior secret from data owners and vice-versa. The methodology will be illustrated with a variety of examples, together with a discussion of the ongoing direction of the work.

Contact sunil.chhita@durham.ac.uk for more information


Arithmetic Study Group

Statistics Seminars: Towards Encrypted Inference for Arbitrary Models

Presented by Louis Aslett , Durham University

9 October 2017 14:00 in CM221

There has been substantial progress in development of statistical methods which are amenable to computation with modern cryptographic techniques, such as homomorphic encryption. This has enabled fitting and/or prediction of models in areas from classification and regression through to genome wide association studies. However, these are techniques devised to address specific models in specific settings, with the broader challenge of an approach to inference for arbitrary models and arbitrary data sets receiving less attention. This talk will discuss very recent results from ongoing work towards an approach which may allow theoretically arbitrary low dimensional models to be fitted fully encrypted, keeping the model and prior secret from data owners and vice-versa. The methodology will be illustrated with a variety of examples, together with a discussion of the ongoing direction of the work.

Contact sunil.chhita@durham.ac.uk for more information


Centre for Particle Theory Colloquia

Statistics Seminars: Towards Encrypted Inference for Arbitrary Models

Presented by Louis Aslett , Durham University

9 October 2017 14:00 in CM221

There has been substantial progress in development of statistical methods which are amenable to computation with modern cryptographic techniques, such as homomorphic encryption. This has enabled fitting and/or prediction of models in areas from classification and regression through to genome wide association studies. However, these are techniques devised to address specific models in specific settings, with the broader challenge of an approach to inference for arbitrary models and arbitrary data sets receiving less attention. This talk will discuss very recent results from ongoing work towards an approach which may allow theoretically arbitrary low dimensional models to be fitted fully encrypted, keeping the model and prior secret from data owners and vice-versa. The methodology will be illustrated with a variety of examples, together with a discussion of the ongoing direction of the work.

Contact sunil.chhita@durham.ac.uk for more information


Computing Seminars/Talks

Statistics Seminars: Towards Encrypted Inference for Arbitrary Models

Presented by Louis Aslett , Durham University

9 October 2017 14:00 in CM221

There has been substantial progress in development of statistical methods which are amenable to computation with modern cryptographic techniques, such as homomorphic encryption. This has enabled fitting and/or prediction of models in areas from classification and regression through to genome wide association studies. However, these are techniques devised to address specific models in specific settings, with the broader challenge of an approach to inference for arbitrary models and arbitrary data sets receiving less attention. This talk will discuss very recent results from ongoing work towards an approach which may allow theoretically arbitrary low dimensional models to be fitted fully encrypted, keeping the model and prior secret from data owners and vice-versa. The methodology will be illustrated with a variety of examples, together with a discussion of the ongoing direction of the work.

Contact sunil.chhita@durham.ac.uk for more information


CPT Student Seminar

Statistics Seminars: Towards Encrypted Inference for Arbitrary Models

Presented by Louis Aslett , Durham University

9 October 2017 14:00 in CM221

There has been substantial progress in development of statistical methods which are amenable to computation with modern cryptographic techniques, such as homomorphic encryption. This has enabled fitting and/or prediction of models in areas from classification and regression through to genome wide association studies. However, these are techniques devised to address specific models in specific settings, with the broader challenge of an approach to inference for arbitrary models and arbitrary data sets receiving less attention. This talk will discuss very recent results from ongoing work towards an approach which may allow theoretically arbitrary low dimensional models to be fitted fully encrypted, keeping the model and prior secret from data owners and vice-versa. The methodology will be illustrated with a variety of examples, together with a discussion of the ongoing direction of the work.

Contact sunil.chhita@durham.ac.uk for more information


Departmental Research Colloquium

Statistics Seminars: Towards Encrypted Inference for Arbitrary Models

Presented by Louis Aslett , Durham University

9 October 2017 14:00 in CM221

There has been substantial progress in development of statistical methods which are amenable to computation with modern cryptographic techniques, such as homomorphic encryption. This has enabled fitting and/or prediction of models in areas from classification and regression through to genome wide association studies. However, these are techniques devised to address specific models in specific settings, with the broader challenge of an approach to inference for arbitrary models and arbitrary data sets receiving less attention. This talk will discuss very recent results from ongoing work towards an approach which may allow theoretically arbitrary low dimensional models to be fitted fully encrypted, keeping the model and prior secret from data owners and vice-versa. The methodology will be illustrated with a variety of examples, together with a discussion of the ongoing direction of the work.

Contact sunil.chhita@durham.ac.uk for more information


Distinguished Lectures and Public Lectures

Statistics Seminars: Towards Encrypted Inference for Arbitrary Models

Presented by Louis Aslett , Durham University

9 October 2017 14:00 in CM221

There has been substantial progress in development of statistical methods which are amenable to computation with modern cryptographic techniques, such as homomorphic encryption. This has enabled fitting and/or prediction of models in areas from classification and regression through to genome wide association studies. However, these are techniques devised to address specific models in specific settings, with the broader challenge of an approach to inference for arbitrary models and arbitrary data sets receiving less attention. This talk will discuss very recent results from ongoing work towards an approach which may allow theoretically arbitrary low dimensional models to be fitted fully encrypted, keeping the model and prior secret from data owners and vice-versa. The methodology will be illustrated with a variety of examples, together with a discussion of the ongoing direction of the work.

Contact sunil.chhita@durham.ac.uk for more information


Geometry and Topology Seminar

Statistics Seminars: Towards Encrypted Inference for Arbitrary Models

Presented by Louis Aslett , Durham University

9 October 2017 14:00 in CM221

There has been substantial progress in development of statistical methods which are amenable to computation with modern cryptographic techniques, such as homomorphic encryption. This has enabled fitting and/or prediction of models in areas from classification and regression through to genome wide association studies. However, these are techniques devised to address specific models in specific settings, with the broader challenge of an approach to inference for arbitrary models and arbitrary data sets receiving less attention. This talk will discuss very recent results from ongoing work towards an approach which may allow theoretically arbitrary low dimensional models to be fitted fully encrypted, keeping the model and prior secret from data owners and vice-versa. The methodology will be illustrated with a variety of examples, together with a discussion of the ongoing direction of the work.

Contact sunil.chhita@durham.ac.uk for more information


Informal HEP Journal club

Statistics Seminars: Towards Encrypted Inference for Arbitrary Models

Presented by Louis Aslett , Durham University

9 October 2017 14:00 in CM221

There has been substantial progress in development of statistical methods which are amenable to computation with modern cryptographic techniques, such as homomorphic encryption. This has enabled fitting and/or prediction of models in areas from classification and regression through to genome wide association studies. However, these are techniques devised to address specific models in specific settings, with the broader challenge of an approach to inference for arbitrary models and arbitrary data sets receiving less attention. This talk will discuss very recent results from ongoing work towards an approach which may allow theoretically arbitrary low dimensional models to be fitted fully encrypted, keeping the model and prior secret from data owners and vice-versa. The methodology will be illustrated with a variety of examples, together with a discussion of the ongoing direction of the work.

Contact sunil.chhita@durham.ac.uk for more information


Maths HEP Lunchtime Seminars

Statistics Seminars: Towards Encrypted Inference for Arbitrary Models

Presented by Louis Aslett , Durham University

9 October 2017 14:00 in CM221

There has been substantial progress in development of statistical methods which are amenable to computation with modern cryptographic techniques, such as homomorphic encryption. This has enabled fitting and/or prediction of models in areas from classification and regression through to genome wide association studies. However, these are techniques devised to address specific models in specific settings, with the broader challenge of an approach to inference for arbitrary models and arbitrary data sets receiving less attention. This talk will discuss very recent results from ongoing work towards an approach which may allow theoretically arbitrary low dimensional models to be fitted fully encrypted, keeping the model and prior secret from data owners and vice-versa. The methodology will be illustrated with a variety of examples, together with a discussion of the ongoing direction of the work.

Contact sunil.chhita@durham.ac.uk for more information


Pure Maths Colloquium

Statistics Seminars: Towards Encrypted Inference for Arbitrary Models

Presented by Louis Aslett , Durham University

9 October 2017 14:00 in CM221

There has been substantial progress in development of statistical methods which are amenable to computation with modern cryptographic techniques, such as homomorphic encryption. This has enabled fitting and/or prediction of models in areas from classification and regression through to genome wide association studies. However, these are techniques devised to address specific models in specific settings, with the broader challenge of an approach to inference for arbitrary models and arbitrary data sets receiving less attention. This talk will discuss very recent results from ongoing work towards an approach which may allow theoretically arbitrary low dimensional models to be fitted fully encrypted, keeping the model and prior secret from data owners and vice-versa. The methodology will be illustrated with a variety of examples, together with a discussion of the ongoing direction of the work.

Contact sunil.chhita@durham.ac.uk for more information


Statistics Seminars

Statistics Seminars: Towards Encrypted Inference for Arbitrary Models

Presented by Louis Aslett , Durham University

9 October 2017 14:00 in CM221

There has been substantial progress in development of statistical methods which are amenable to computation with modern cryptographic techniques, such as homomorphic encryption. This has enabled fitting and/or prediction of models in areas from classification and regression through to genome wide association studies. However, these are techniques devised to address specific models in specific settings, with the broader challenge of an approach to inference for arbitrary models and arbitrary data sets receiving less attention. This talk will discuss very recent results from ongoing work towards an approach which may allow theoretically arbitrary low dimensional models to be fitted fully encrypted, keeping the model and prior secret from data owners and vice-versa. The methodology will be illustrated with a variety of examples, together with a discussion of the ongoing direction of the work.

Contact sunil.chhita@durham.ac.uk for more information


Stats4Grads

Statistics Seminars: Towards Encrypted Inference for Arbitrary Models

Presented by Louis Aslett , Durham University

9 October 2017 14:00 in CM221

There has been substantial progress in development of statistical methods which are amenable to computation with modern cryptographic techniques, such as homomorphic encryption. This has enabled fitting and/or prediction of models in areas from classification and regression through to genome wide association studies. However, these are techniques devised to address specific models in specific settings, with the broader challenge of an approach to inference for arbitrary models and arbitrary data sets receiving less attention. This talk will discuss very recent results from ongoing work towards an approach which may allow theoretically arbitrary low dimensional models to be fitted fully encrypted, keeping the model and prior secret from data owners and vice-versa. The methodology will be illustrated with a variety of examples, together with a discussion of the ongoing direction of the work.

Contact sunil.chhita@durham.ac.uk for more information


Information about seminars for the current academic year. For information on previous years' seminars please see the seminar archives pages.