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YOW! Data
Respecting Privacy with ‘Look alike’ data sets
Tim Garnsey - Director - Verge Labs
“Data is the new oil”
Clive Humby
Respecting Privacy with Look alike data sets Tim Garnsey - Director - - PowerPoint PPT Presentation
Respecting Privacy with Look alike data sets Tim Garnsey - Director - - PowerPoint PPT Presentation
YOW! Data Respecting Privacy with Look alike data sets Tim Garnsey - Director - Verge Labs Data is the new oil Clive Humby What makes refining hard? 1. Corporate competitive advantage 2. Peoples privacy Competitive
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What makes refining hard?
- 1. Corporate competitive advantage
- 2. People’s privacy
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Competitive advantage
User looks for content Generating content in the process Making content attractive
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People’s privacy
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Analysing data
- 1. Care about relationships within subjects (in aggregate)
- 2. Care about relationships across subjects (in aggregate)
- 3. Don’t care about subjects
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Analysing data
Date Name Business * Lat Long City 2017-06-15 Nicole Thelma's Filipino Restaurant 5 36.0631
- 115.038
Henderson NV 2015-09-23 Matt Cabo's Mexican Cuisine and Cantina 1 35.0854
- 80.8476
Charlotte NC 2016-05-03 Maddie The Parlor 3 33.5094
- 112.04
Phoenix AZ 2017-08-08 Nick DC Steak House 1 33.3024
- 111.842
Chandler AZ 2017-07-16 Keith Kneaders Bakery & Cafe 1 33.6266
- 111.896
Scottsdale AZ 2013-02-10 Mike Hickory Tavern 4 35.1019
- 80.9912
Charlotte NC 2016-08-23 Michael Wendy's Noodle Cafe 3 36.1275
- 115.225
Las Vegas NV 2017-02-10 Petrina Clones 4 Patients ONLY 1 36.2858
- 115.285
Las Vegas NV 2016-01-03 Maddie Chicks With Spiritual Gifts 5 33.4689
- 112.07
Phoenix AZ 2016-10-19 Sunggin Cholla Prime Steakhouse & Lounge 4 33.454
- 111.886
Scottsdale AZ 2012-01-30 Maribeth Urban Cookies Bakeshop 5 33.4742
- 112.065
Glendale AZ 2016-11-04 Jaime In-N-Out Burger 5 33.508
- 112.266
Phoenix AZ
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Analysing data
Date Name Business * Lat Long City 2017-11-22 Angela Fat Tuesday 5 36.1095
- 115.171
Las Vegas NV 2013-09-18 Kenny Yanni's Gyros 5 36.0119
- 115.136
Las Vegas NV 2012-12-07 Joyce Panera Bread 4 33.5805
- 112.122
Glendale AZ 2017-12-04 Kenny Scallywags 5 43.6878
- 79.3945
Toronto ON 2012-11-15 Abby Tip of the Tail Grooming 1 41.4317
- 81.8867
North Olmsted OH 2011-02-26 Laura Thai Express 1 43.6132
- 79.5558
Etobicoke ON 2017-10-11 Liane Armando's Mexican Food 4 33.6842
- 112.107
Phoenix AZ 2017-05-17 Laura Momo Hair Salon 5 43.7039
- 79.3979
Toronto ON 2014-01-02 Caitlin Chopstix Express 3 36.1425
- 115.209
Las Vegas NV 2017-03-04 Han Take Over Lease 5 40.4403
- 79.9863
Pittsburgh PA 2016-10-21 Will Betty's Flower Shop 1 36.2384
- 115.155
North Las Vegas NV 2013-08-19 Steve Rise Biscuits & Donuts 3 33.4923
- 111.924
Scottsdale AZ
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Analysing data
- 1. Care about relationships within subjects (in aggregate)
- 2. Care about relationships across subjects (in aggregate)
- 3. Don’t care about subjects
… really need a “very statistically similar” data set
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Copying machines
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Use GANs …
0.3 0.6 0.1
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Use differential privacy …
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… and join them together
0.3 0.6 0.1
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Check-in data
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Check-in data
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Zoom
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Tips and tricks
There is a universe here, but getting started is easy:
- 1. Blogs, SO and wikipedia
- 2. Convert to floating point (cities, text)
- 3. Start small (there is always something that is valuable
enough)
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