Jan Henrik Ziegeldorf

Oxford

I came to Oxford in July 2014 to present my poster “Privacy-preserving Indoor Localization” [1] at ACM WiSec’14,  preliminary work on enhancing the indoor localization system proposed by my colleague and friend Nico [2] with strong privacy guarantees using Secure Two-Party Computation (STC). Much later, this initial idea  lead to two more papers [3] [4] and, more importantly, a half-marathon in hot and sunny Scottsdale, Arizona.

Total distance: 21.19 km
Max elevation: 71 m
Total climbing: 118 m

Where to run? The English… I don’t like how they do breakfast but I certainly like how they do parks. And Oxford has quite a lot of them, counting in all the semi-groomed meadows around it. That’s where I chose to run. And also, for the lack of a magic flying broom*, to avoid the inner city.

  • Port Meadow and River Thames: Starting from St. Annes College, the location of the conference, I headed off to the north west towards the Thames into the Port Meadow, a huge flat grassland where you can really let the dogs loose (you literally have to be aware of dogs let loose, there).
  • Grandpont Nature Park: This gem was laying right on my route. Unfortunately, having focussed on following the Thames, I missed it completely.
  • Christ Church Meadow: Circling Oxford on the south, I ran into this other really nice meadow. It’s closer to the city and framed by a couple of nice old buildings, e.g., Christ Church (who would’ve guessed). The Boathouses Walk on the far south is recommendable, as is the Deadman’s Walk on the north (not least for its name). 
  • Botanic Garden: From Christ Church Meadow, a quick sprint along Merton College took me to the Oxford Botanic Garden where I paid a quick visit to the park bench made famous by Philip Pullman’s His Dark Materials book trilogy**.  
  • University parks:  Completing my counter-clockwise circle around Oxford, I rewarded myself with a finish through the University parks, passing Parsons’s Pleasure, which was also mine.

When to run? Oxford is a year-around running destination. Since Ale is served at room temperature, you might not want to go during summer or resort to another refreshing beverage after your run, which is probably a great idea anyways.


* The (mostly awful) movie adoption of the (mostly great) Harry Potter book series has been filmed on different locations in Oxford.
** I read them as a child and again as an adult. They’re still among my favorites. They do contain a lot of references to John Milton’s Paradise Lost, whose 12 books of up to 1200 verses I never got around to read. If you have, please tell me if it’s worth it.

[1] [pdf] J. H. Ziegeldorf, N. Viol, M. Henze, and K. Wehrle, “POSTER: Privacy-preserving Indoor Localization,” WiSec’14, 2014.
[Bibtex]
@article{ziegeldorf2014poster,
  Author = {Ziegeldorf, Jan Henrik and Viol, Nicolai and Henze, Martin and Wehrle, Klaus},
  Date-Added = {2018-10-06 16:12:13 +0000},
  Date-Modified = {2018-10-06 16:12:13 +0000},
  Journal = {WiSec'14},
  Title = {POSTER: Privacy-preserving Indoor Localization},
  Year = {2014}}
[2] N. Viol, Á. B. Jó. Link, H. Wirtz, D. Rothe, and K. Wehrle, “Hidden Markov model-based 3D path-matching using raytracing-generated Wi-Fi models,” in Indoor Positioning and Indoor Navigation (IPIN), 2012 International Conference on, 2012, pp. 1-10.
[Bibtex]
@inproceedings{viol2012hidden,
  Author = {Viol, Nicolai and Link, J{\'o} {\'A}gila Bitsch and Wirtz, Hanno and Rothe, Dirk and Wehrle, Klaus},
  Booktitle = {Indoor Positioning and Indoor Navigation (IPIN), 2012 International Conference on},
  Date-Added = {2018-10-14 13:00:23 +0000},
  Date-Modified = {2018-10-14 13:00:23 +0000},
  Organization = {IEEE},
  Pages = {1--10},
  Title = {Hidden Markov model-based 3D path-matching using raytracing-generated Wi-Fi models},
  Year = {2012}}
[3] [pdf] [doi] J. H. Ziegeldorf, J. Metzke, J. Rüth, M. Henze, and K. Wehrle, “Privacy-Preserving HMM Forward Computation,” in Proceedings of the Seventh ACM on Conference on Data and Application Security and Privacy, New York, NY, USA, 2017, pp. 83-94.
[Bibtex]
@inproceedings{ziegeldorf2017priward,
  Acceptancerate = {16 %},
  Acmid = {3029816},
  Address = {New York, NY, USA},
  Author = {Ziegeldorf, Jan Henrik and Metzke, Jan and R\"{u}th, Jan and Henze, Martin and Wehrle, Klaus},
  Booktitle = {Proceedings of the Seventh ACM on Conference on Data and Application Security and Privacy},
  Date-Added = {2018-10-06 16:03:52 +0000},
  Date-Modified = {2018-10-10 05:36:57 +0000},
  Doi = {10.1145/3029806.3029816},
  Isbn = {978-1-4503-4523-1},
  Keywords = {forward algorithm, garbled circuits, hidden Markov models, privacy-preserving protocols, secure two-party computation},
  Location = {Scottsdale, Arizona, USA},
  Note = {Outstanding Paper Award},
  Numpages = {12},
  Pages = {83--94},
  Publisher = {ACM},
  Series = {CODASPY '17},
  Title = {Privacy-Preserving HMM Forward Computation},
  Url = {http://doi.acm.org/10.1145/3029806.3029816},
  Year = {2017},
  Bdsk-Url-1 = {http://doi.acm.org/10.1145/3029806.3029816},
  Bdsk-Url-2 = {https://doi.org/10.1145/3029806.3029816}}
[4] [pdf] J. H. Ziegeldorf, J. Metzke, and K. Wehrle, “SHIELD: A Framework for Efficient and Secure Machine Learning Classification in Constrained Environments,” in Proceedings of the 34rd Annual Computer Security Applications Conference, New York, NY, USA, 2018, pp. 1-15.
[Bibtex]
@inproceedings{ziegeldorf2018shield,
  Abstract = {Machine learning classification has enabled many innovative services, e.g., in medicine, biometrics, and finance.
Current practices of sharing sensitive input data or classification models, however, causes privacy concerns among the users and business risk among the providers.
In this work, we resolve the conflict between privacy and business interests using Secure Two-Party Computation.
Concretely, we propose SHIELD, a framework for efficient, and accurate machine learning classification with security in the semi-honest model.
Building on SHIELD, we realize several widely used classifiers and real-world use cases that compare favorably against related work.
Departing definitively from prior works, all of SHIELD's protocols are designed from the ground up to enable secure outsourcing to untrusted computation clouds enabling even constrained devices to handle our most complex use cases in (milli)seconds.},
  Acceptancerate = {21 %},
  Address = {New York, NY, USA},
  Author = {Ziegeldorf, Jan Henrik and Metzke, Jan and Wehrle, Klaus},
  Booktitle = {Proceedings of the 34rd Annual Computer Security Applications Conference},
  Date-Added = {2018-10-10 14:11:45 +0000},
  Date-Modified = {2018-10-10 14:20:39 +0000},
  Location = {San Juan, Puerto Rico, USA},
  Month = {December},
  Pages = {1--15},
  Publisher = {ACM},
  Series = {ACSAC'18},
  Title = {SHIELD: A Framework for Efficient and Secure Machine Learning Classification in Constrained Environments},
  Year = {2018},
  Bdsk-Url-1 = {https://doi.org/10.1145/3029806.3029816}}

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