Pinning Down Abuse on Google Maps Univ of California, San Diego - - PowerPoint PPT Presentation

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Pinning Down Abuse on Google Maps Univ of California, San Diego - - PowerPoint PPT Presentation

Pinning Down Abuse on Google Maps Univ of California, San Diego Danny Y. Huang, Kirill Levchenko, Alex C. Snoeren Google Doug Grundman, Kurt Thomas, Abhishek Kumar, Elie Bursztein Presented at the International World Wide Web Conference in


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Pinning Down Abuse

  • n Google Maps

Univ of California, San Diego

Danny Y. Huang, Kirill Levchenko, Alex C. Snoeren

Google

Doug Grundman, Kurt Thomas, Abhishek Kumar, Elie Bursztein

Presented at the International World Wide Web Conference in Perth, Australia on April 7, 2017.

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Maps SEO is based on physical proximity

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Traditional SEO

Ranked by relevance Example: pharma

Maps SEO

Ranked by proximity Example: locksmiths How many and where? How to make money? How to evade detection? Impact on users?

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7 locksmiths near me

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“Locksmith” located at UPS Store

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Fake locksmiths are an old problem

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Jan 2016 False sense of proximity Monetize from inferior service

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Overview of abuse on Google Maps

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Violation of ToS Caused harm to users

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Overview of abuse on Google Maps

Abusive listings 2014 - 2015 15% reached users

Fake Locations (40%)

locksmiths, plumbers, garage door openers

Affiliate Fraud (13%)

hotels, restaurants

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Misc (47%)

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Affiliate fraudsters hijack hotel listings

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Paying for referral traffic Damages to branding Reputable travel agency

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hotels % of abusive listings in each country

Abuse varies across countries

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locksmiths plumbers, etc 0% 20% 40% 60% 100% USA India 80% % of abusive listings in each country

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How did scammers evade Google’s detection?

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Creating a free listing on Google Maps

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Verification via mail

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80% abusive listings verified through mail

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Creating listings by buying fake locations

Residential Commercial

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Verifying listings using the same address

123 Main St 123 Main St Suite 456 123 Main St #456 #456 123 Main St l23 Main St

Verify multiple listings

25% of abusive listings used this trick

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Impose a cutoff?

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Abusive listings verified at the same address

>1,000 101 - 1,000 11 - 100 2 - 10 1 # Listings at same address 0% 20% 40% 60% 80% 100% Probability of abusive listings

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e.g. legit doctor listings at hospital

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Impact on end users?

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Quantifying impact on users # of abusive search results # of search results

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Locksmiths*

11%

* 11% of local search results for locksmiths in the US were abusive during our analysis period.

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Search results that are abusive

Locksmiths

11%

Plumbers, etc

0.4%

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New York, NY

16%

West Harrison, NY*

84%

Restaurants

1%

Hotels

1%

* 84% of search results for locksmiths in West Harrison, NY were abusive during our analysis period.

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Pie chart: abusive results that are restaurants

Locksmiths

11%

Plumbers, etc

0.4%

Restaurants

1%

Hotels

1%

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* For every 100 random abusive search results in the US, 2 were abusive locksmiths, while 47 were abusive restaurants.

* *

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Overall search results that are abusive

Locksmiths

11%

Plumbers, etc

0.4%

Restaurants

1%

Hotels

1%

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0.5%

  • f search results in US were abusive*

* Out of 1000 random search results in the US, only 5 were abusive results during our analysis period.

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SEO based on physical proximity Fake locations vs affiliate fraud Hard to verify addresses at scale Improved defenses by Google; abusive listings down by 70% from peak Summary

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Thank you!

More info: http://sysnet.ucsd.edu/~dhuang/

Univ of California, San Diego

Danny Y. Huang, Kirill Levchenko, Alex C. Snoeren

Google

Doug Grundman, Kurt Thomas, Abhishek Kumar, Elie Bursztein