Diffusion of User Tracking Data in the Online Advertising Ecosystem - - PowerPoint PPT Presentation

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Diffusion of User Tracking Data in the Online Advertising Ecosystem - - PowerPoint PPT Presentation

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Diffusion of User Tracking Data in the Online Advertising Ecosystem Muhammad Ahmad Bashir and Christo Wilson Your Digital Privacy Footprint 2 Your Digital Privacy Footprint 2 Your Digital Privacy Footprint 2 Your Digital Privacy

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SLIDE 1

Diffusion of User Tracking Data in the Online Advertising Ecosystem

Muhammad Ahmad Bashir and Christo Wilson

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SLIDE 2

Your Digital Privacy Footprint

2

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SLIDE 3

Your Digital Privacy Footprint

2

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SLIDE 4

Your Digital Privacy Footprint

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SLIDE 5

Your Digital Privacy Footprint

2

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SLIDE 6

Your Digital Privacy Footprint

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SLIDE 7

Your Digital Privacy Footprint

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SLIDE 8

Your Digital Privacy Footprint

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SLIDE 9

Your Digital Privacy Footprint

2

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SLIDE 10

Ads Under Real Time Bidding (RTB)

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SLIDE 11

Ads Under Real Time Bidding (RTB)

3

Ad 
 Space Content User

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SLIDE 12

Ads Under Real Time Bidding (RTB)

3

Ad 
 Space Content User 1

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SLIDE 13

Ads Under Real Time Bidding (RTB)

3

Ad 
 Space Content User 1 Supply Side Platforms 
 (SSPs) 2

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SLIDE 14

Ads Under Real Time Bidding (RTB)

3

Ad 
 Space Content User 1 Supply Side Platforms 
 (SSPs) Ad Exchanges 2 3

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SLIDE 15

Ads Under Real Time Bidding (RTB)

3

Ad 
 Space Content User 1 Supply Side Platforms 
 (SSPs) Ad Exchanges 2 3 4 Demand Side Platforms 
 (DSPs)

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SLIDE 16

Ads Under Real Time Bidding (RTB)

3

Ad 
 Space Content User 1 Supply Side Platforms 
 (SSPs) Ad Exchanges 2 3 4 Demand Side Platforms 
 (DSPs) Advertisers 5

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SLIDE 17

Ads Under Real Time Bidding (RTB)

3

Ad 
 Space Content User 1 Supply Side Platforms 
 (SSPs) Ad Exchanges 2 3 4 Demand Side Platforms 
 (DSPs) 6 Advertisers 5

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SLIDE 18

Ads Under Real Time Bidding (RTB)

3

Ad 
 Space Content User 1 Supply Side Platforms 
 (SSPs) Ad Exchanges 2 3 4 Demand Side Platforms 
 (DSPs) 6 Advertisers 5

Although RightMedia does not win the auction, 
 it still sees the impression

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SLIDE 19

Ads Under Real Time Bidding (RTB)

3

Ad 
 Space Content User 1 Supply Side Platforms 
 (SSPs) Ad Exchanges 2 3 4 Demand Side Platforms 
 (DSPs) 6 Advertisers 5

Although RightMedia does not win the auction, 
 it still sees the impression

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SLIDE 20

Ads Under Real Time Bidding (RTB)

3

Ad 
 Space Content User 1 Supply Side Platforms 
 (SSPs) Ad Exchanges 2 3 4 Demand Side Platforms 
 (DSPs) 6 Advertisers 5

Although RightMedia does not win the auction, 
 it still sees the impression

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SLIDE 21

4

Adjusted with Real Time Bidding

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SLIDE 22

4

Adjusted with Real Time Bidding

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SLIDE 23

4

Adjusted with Real Time Bidding

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SLIDE 24

4

Adjusted with Real Time Bidding

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SLIDE 25

4

Adjusted with Real Time Bidding

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SLIDE 26

4

Adjusted with Real Time Bidding

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SLIDE 27

4

Adjusted with Real Time Bidding

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SLIDE 28

Goal of the Study

5

Model the Diffusion of Impressions in the Advertising Ecosystem

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SLIDE 29

Goal of the Study

  • We want to take Real Time Bidding (RTB) into account
  • This goes beyond the current techniques being used

5

Model the Diffusion of Impressions in the Advertising Ecosystem

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SLIDE 30

Goal of the Study

  • We want to take Real Time Bidding (RTB) into account
  • This goes beyond the current techniques being used
  • We want to answer the following:

1. What fraction of user impressions are viewed by ad companies? 2. How much ad and tracker blocking extensions help?

5

Model the Diffusion of Impressions in the Advertising Ecosystem

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SLIDE 31

Goal of the Study

  • We want to take Real Time Bidding (RTB) into account
  • This goes beyond the current techniques being used
  • We want to answer the following:

1. What fraction of user impressions are viewed by ad companies? 2. How much ad and tracker blocking extensions help?

5

Model the Diffusion of Impressions in the Advertising Ecosystem

Key Terms:

  • 1. Impressions: Page Visits
  • 2. Publishers: First party websites visited by users (e.g. cnn, bbc, espn)
  • 3. A&A: Advertising and Analytics related companies / domains

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SLIDE 32

Overview

  • 1. Dataset used in our study

  • 2. Our Simulations 

  • 3. Results

  • 4. Ad & Tracker Blocking

6

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SLIDE 33

Dataset Used

7

  • 1. Bashir et al. Tracing Information Flows Between Ad Exchanges Using Retargeted Ads. Usenix Security 2016
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SLIDE 34

Dataset Used

  • We use data from our prior work.1
  • We trained personas using 738 popular e-commerce websites and solicit

retargeted ads through 150 top publishers.

7

  • 1. Bashir et al. Tracing Information Flows Between Ad Exchanges Using Retargeted Ads. Usenix Security 2016
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SLIDE 35

Dataset Used

  • We use data from our prior work.1
  • We trained personas using 738 popular e-commerce websites and solicit

retargeted ads through 150 top publishers.

  • Using this data, we have inclusion chains of all resources

7

  • 1. Bashir et al. Tracing Information Flows Between Ad Exchanges Using Retargeted Ads. Usenix Security 2016
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SLIDE 36

Dataset Used

  • We use data from our prior work.1
  • We trained personas using 738 popular e-commerce websites and solicit

retargeted ads through 150 top publishers.

  • Using this data, we have inclusion chains of all resources

7

  • 1. Bashir et al. Tracing Information Flows Between Ad Exchanges Using Retargeted Ads. Usenix Security 2016

Example Inclusion Chain

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SLIDE 37

From Chains to Graph

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SLIDE 38

From Chains to Graph

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Inclusion Chains

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SLIDE 39

From Chains to Graph

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Publishers A&A (Advertising and Analytics)

Inclusion Chains

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SLIDE 40

From Chains to Graph

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Publishers A&A (Advertising and Analytics)

Graph Representation Inclusion Chains

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SLIDE 41

From Chains to Graph

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Publishers A&A (Advertising and Analytics)

Graph Representation Inclusion Chains

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SLIDE 42

From Chains to Graph

8

1 1

Publishers A&A (Advertising and Analytics)

Graph Representation

1

Inclusion Chains

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SLIDE 43

From Chains to Graph

8

1 1

Publishers A&A (Advertising and Analytics)

Graph Representation

1

Inclusion Chains

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SLIDE 44

From Chains to Graph

8

1 1 1 1

Publishers A&A (Advertising and Analytics)

Graph Representation

1

Inclusion Chains

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SLIDE 45

From Chains to Graph

8

1 1 1 1 2

Publishers A&A (Advertising and Analytics)

Graph Representation Inclusion Chains

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SLIDE 46

From Chains to Graph

8

1 1 1 1 2

Publishers A&A (Advertising and Analytics)

Graph Representation

Nodes: Publishers or A&A domains 
 Edges: Publisher —> A&A 
 A&A —> A&A

Inclusion Chains

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SLIDE 47

From Chains to Graph

8

1 1 1 1 2

Publishers A&A (Advertising and Analytics)

Graph Representation

Nodes: Publishers or A&A domains 
 Edges: Publisher —> A&A 
 A&A —> A&A

Inclusion Chains

Nodes:

  • Total: ~1.9K
  • A&A: ~1K

Edges:

  • Total: ~26K
  • Pub —> A&A: ~10.5K
  • A&A —> A&A: ~15.5K
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SLIDE 48

Some Properties of the Graph

9

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SLIDE 49

Some Properties of the Graph

  • The graph is very dense.

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SLIDE 50

Some Properties of the Graph

  • The graph is very dense.
  • No distinct communities
  • Web is not necessary balkanized into distinct groups


9

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SLIDE 51

Some Properties of the Graph

  • The graph is very dense.
  • No distinct communities
  • Web is not necessary balkanized into distinct groups

  • Expected top nodes with PageRank and Betweenness Centrality

9

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SLIDE 52

Goal of the Study

  • 1. What fraction of user impressions are viewed by ad companies?

  • 2. How much ad and tracker blocking extensions help?

10

Model the Diffusion of Impressions in the Advertising Ecosystem

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SLIDE 53

Our Simulation Setup

11

[1]. Burken et al. User centric walk: An integrated approach for modeling the browsing behavior of users on the web. ASS 2005

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SLIDE 54

Our Simulation Setup

We simulate browsing traces for 200 users using method from [1].

11

[1]. Burken et al. User centric walk: An integrated approach for modeling the browsing behavior of users on the web. ASS 2005

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SLIDE 55

Our Simulation Setup

We simulate browsing traces for 200 users using method from [1]. 1. User generates an impression on N selected publishers.

11

[1]. Burken et al. User centric walk: An integrated approach for modeling the browsing behavior of users on the web. ASS 2005

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SLIDE 56

Our Simulation Setup

We simulate browsing traces for 200 users using method from [1]. 1. User generates an impression on N selected publishers. 2. Impressions are forwarded to A&A domains via:

  • A. Direct Propagation:
  • Present on publisher or won RTB auction. Observable (goes through the browser)
  • B. Indirect Propagation:
  • A&A domains learn impressions through RTB participation. Non-observable

11

[1]. Burken et al. User centric walk: An integrated approach for modeling the browsing behavior of users on the web. ASS 2005

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SLIDE 57

Our Simulation Setup

We simulate browsing traces for 200 users using method from [1]. 1. User generates an impression on N selected publishers. 2. Impressions are forwarded to A&A domains via:

  • A. Direct Propagation:
  • Present on publisher or won RTB auction. Observable (goes through the browser)
  • B. Indirect Propagation:
  • A&A domains learn impressions through RTB participation. Non-observable

11

[1]. Burken et al. User centric walk: An integrated approach for modeling the browsing behavior of users on the web. ASS 2005

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SLIDE 58

Our Simulation Setup

We simulate browsing traces for 200 users using method from [1]. 1. User generates an impression on N selected publishers. 2. Impressions are forwarded to A&A domains via:

  • A. Direct Propagation:
  • Present on publisher or won RTB auction. Observable (goes through the browser)
  • B. Indirect Propagation:
  • A&A domains learn impressions through RTB participation. Non-observable

11

[1]. Burken et al. User centric walk: An integrated approach for modeling the browsing behavior of users on the web. ASS 2005

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SLIDE 59

Our Simulation Setup

We simulate browsing traces for 200 users using method from [1]. 1. User generates an impression on N selected publishers. 2. Impressions are forwarded to A&A domains via:

  • A. Direct Propagation:
  • Present on publisher or won RTB auction. Observable (goes through the browser)
  • B. Indirect Propagation:
  • A&A domains learn impressions through RTB participation. Non-observable

11

[1]. Burken et al. User centric walk: An integrated approach for modeling the browsing behavior of users on the web. ASS 2005

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SLIDE 60

Our Simulation Setup

We simulate browsing traces for 200 users using method from [1]. 1. User generates an impression on N selected publishers. 2. Impressions are forwarded to A&A domains via:

  • A. Direct Propagation:
  • Present on publisher or won RTB auction. Observable (goes through the browser)
  • B. Indirect Propagation:
  • A&A domains learn impressions through RTB participation. Non-observable

3. RTB winner is decided based on probability (function of edge weights).

11

[1]. Burken et al. User centric walk: An integrated approach for modeling the browsing behavior of users on the web. ASS 2005

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SLIDE 61

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Publisher Exchange DSP/Advertiser

Node Type

Direct Indirect

Activation

p1 p2 e2 e1 a3 a2 a1 a5 a4

Simulation Example (RTB Constrained)

100 100 10 90 80 20 5 95

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SLIDE 62

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Publisher Exchange DSP/Advertiser

Node Type

Direct Indirect

Activation

p1 p2 e2 e1 a3 a2 a1 a5 a4

Simulation Example (RTB Constrained)

We manually select 37 exchanges which are allowed to forward indirect impressions to solicit bids during RTB

100 100 10 90 80 20 5 95

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SLIDE 63

12

Publisher Exchange DSP/Advertiser

Node Type

Direct Indirect

Activation

p1 p2 e2 e1 a3 a2 a1 a5 a4 p1

Simulation Example (RTB Constrained)

We manually select 37 exchanges which are allowed to forward indirect impressions to solicit bids during RTB

100 100 10 90 80 20 5 95

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SLIDE 64

12

Publisher Exchange DSP/Advertiser

Node Type

Direct Indirect

Activation

p1 p2 e2 e1 a3 a2 a1 a5 a4 p1 e1

1

Simulation Example (RTB Constrained)

We manually select 37 exchanges which are allowed to forward indirect impressions to solicit bids during RTB

100 100 10 90 80 20 5 95

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SLIDE 65

12

Publisher Exchange DSP/Advertiser

Node Type

Direct Indirect

Activation

p1 p2 e2 e1 a3 a2 a1 a5 a4 p1 e1 a2 a1

1 1 1

Simulation Example (RTB Constrained)

We manually select 37 exchanges which are allowed to forward indirect impressions to solicit bids during RTB

100 100 10 90 80 20 5 95

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SLIDE 66

12

Publisher Exchange DSP/Advertiser

Node Type

Direct Indirect

Activation

p1 p2 e2 e1 a3 a2 a1 a5 a4 p1 e1 a2 a1 a5 a4

1 1 1 1

Simulation Example (RTB Constrained)

We manually select 37 exchanges which are allowed to forward indirect impressions to solicit bids during RTB

100 100 10 90 80 20 5 95

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SLIDE 67

12

Publisher Exchange DSP/Advertiser

Node Type

Direct Indirect

Activation

p1 p2 e2 e1 a3 a2 a1 a5 a4 p1 p2 e1 a2 a1 a5 a4

1 1 1 1

Simulation Example (RTB Constrained)

We manually select 37 exchanges which are allowed to forward indirect impressions to solicit bids during RTB

100 100 10 90 80 20 5 95

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SLIDE 68

12

Publisher Exchange DSP/Advertiser

Node Type

Direct Indirect

Activation

p1 p2 e2 e1 a3 a2 a1 a5 a4 p1 p2 e2 e1 a2 a1 a5 a4

1 1 1 1 1

Simulation Example (RTB Constrained)

We manually select 37 exchanges which are allowed to forward indirect impressions to solicit bids during RTB

100 100 10 90 80 20 5 95

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SLIDE 69

12

Publisher Exchange DSP/Advertiser

Node Type

Direct Indirect

Activation

p1 p2 e2 e1 a3 a2 a1 a5 a4 p1 p2 e2 e1 a3 a2 a1 a5 a4

1 1 1 1 1

Simulation Example (RTB Constrained)

We manually select 37 exchanges which are allowed to forward indirect impressions to solicit bids during RTB

100 100 10 90 80 20 5 95

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SLIDE 70

12

Publisher Exchange DSP/Advertiser

Node Type

Direct Indirect

Activation

p1 p2 e2 e1 a3 a2 a1 a5 a4 p1 p2 e2 e1 a3 a2 a1 a5 a4

1 1 1 2 1 1

Simulation Example (RTB Constrained)

We manually select 37 exchanges which are allowed to forward indirect impressions to solicit bids during RTB

100 100 10 90 80 20 5 95

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SLIDE 71

12

Publisher Exchange DSP/Advertiser

Node Type

Direct Indirect

Activation

p1 p2 e2 e1 a3 a2 a1 a5 a4 p1 p2 e2 e1 a3 a2 a1 a5 a4

1 1 1 2 1 2

Simulation Example (RTB Constrained)

We manually select 37 exchanges which are allowed to forward indirect impressions to solicit bids during RTB

100 100 10 90 80 20 5 95

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SLIDE 72

13

Impressions Observed

0.2 0.4 0.6 0.8 1 0.2 0.4 0.6 0.8 1 Fraction of A&A Domains (CDF) Fraction of Impressions Observed Cookie Matching-Only RTB Constrained RTB Relaxed

We have 3 simulation models:

  • 1. RTB-Relaxed: Upper-bound
  • 2. Cookie-Matching: Lower-bound
  • 3. RTB-Constrained: Realistic Scenario
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SLIDE 73

13

Impressions Observed

0.2 0.4 0.6 0.8 1 0.2 0.4 0.6 0.8 1 Fraction of A&A Domains (CDF) Fraction of Impressions Observed Cookie Matching-Only RTB Constrained RTB Relaxed

We have 3 simulation models:

  • 1. RTB-Relaxed: Upper-bound
  • 2. Cookie-Matching: Lower-bound
  • 3. RTB-Constrained: Realistic Scenario
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SLIDE 74

13

Impressions Observed

0.2 0.4 0.6 0.8 1 0.2 0.4 0.6 0.8 1 Fraction of A&A Domains (CDF) Fraction of Impressions Observed Cookie Matching-Only RTB Constrained RTB Relaxed

We have 3 simulation models:

  • 1. RTB-Relaxed: Upper-bound
  • 2. Cookie-Matching: Lower-bound
  • 3. RTB-Constrained: Realistic Scenario
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SLIDE 75

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Take Away

  • 1. RTB-Constrained is very close to

RTB-Relaxed

  • 2. 10% A&A see more than 90% of

impressions in RTB-Constrained

Impressions Observed

0.2 0.4 0.6 0.8 1 0.2 0.4 0.6 0.8 1 Fraction of A&A Domains (CDF) Fraction of Impressions Observed Cookie Matching-Only RTB Constrained RTB Relaxed

We have 3 simulation models:

  • 1. RTB-Relaxed: Upper-bound
  • 2. Cookie-Matching: Lower-bound
  • 3. RTB-Constrained: Realistic Scenario
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SLIDE 76

Effect of Blocking Extensions

14

DoubleClick OpenX
 PubMatic

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SLIDE 77

Effect of Blocking Extensions

14

DoubleClick OpenX
 PubMatic

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SLIDE 78

Effect of Blocking Extensions

14

DoubleClick OpenX
 PubMatic

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SLIDE 79

Effect of Blocking Extensions

14

DoubleClick OpenX
 PubMatic

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SLIDE 80

Effect of Blocking Extensions

14

DoubleClick OpenX
 PubMatic

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SLIDE 81

Effect of Blocking Extensions

14

DoubleClick OpenX
 PubMatic

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SLIDE 82

Effect of Blocking Extensions

14

DoubleClick OpenX
 PubMatic

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SLIDE 83

Effect of Blocking Extensions

14

DoubleClick OpenX
 PubMatic

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SLIDE 84

Impressions With Blocking

15

0.2 0.4 0.6 0.8 1 0.2 0.4 0.6 0.8 1 Fraction of A&A Domains (CDF) Fraction of Impressions Observed Disconnect Ghostery AdBlock Plus No Removal

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SLIDE 85

Impressions With Blocking

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0.2 0.4 0.6 0.8 1 0.2 0.4 0.6 0.8 1 Fraction of A&A Domains (CDF) Fraction of Impressions Observed Disconnect Ghostery AdBlock Plus No Removal

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SLIDE 86

Impressions With Blocking

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Take Away

  • Disconnect list is most effective.
  • ABP is not effective at all due to

Acceptable Ads program.

  • Due to RTB, impressions are leaked to

A&A domains even with blocking extensions.

0.2 0.4 0.6 0.8 1 0.2 0.4 0.6 0.8 1 Fraction of A&A Domains (CDF) Fraction of Impressions Observed Disconnect Ghostery AdBlock Plus No Removal

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SLIDE 87

Top 10 Domains

16

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SLIDE 88

Top 10 Domains

16

EasyList Disconnect Ghostery AdBlock Plus No Blocking

Impressions Observed (%)

10 20 30 40 50 60 70 80 90 100

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SLIDE 89

Top 10 Domains

16

EasyList Disconnect Ghostery AdBlock Plus No Blocking

Impressions Observed (%)

10 20 30 40 50 60 70 80 90 100

Top 10 companies can view majority of user impressions even with (most) blocking extensions installed

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SLIDE 90

Top 10 Domains

16

EasyList Disconnect Ghostery AdBlock Plus No Blocking

Impressions Observed (%)

10 20 30 40 50 60 70 80 90 100

Top 10 companies can view majority of user impressions even with (most) blocking extensions installed

Domain Impression % google-analytics 97.0 youtube 91.7 quantserve 91.6 scorecardresearch 91.6 skimresources 91.3 twitter 91.1 pinterest 91.0 addthis 90.0 criteo 90.0 bluekai 90.8

Top 10 domains with most observed 
 impressions under AdBlock Plus

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SLIDE 91

Simulation Limitations

  • Our simulation models provide approximations
  • Different users might have different browsing behaviors
  • We only simulate with respect to popular publishers
  • The ecosystem could have changed from when the dataset was collected

(December 2015)

  • Not representative of mobile advertising ecosystem

17

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SLIDE 92

Summary

  • We are the first to provide a model to study the impact of Real Time Bidding (RTB)
  • n user privacy.
  • Ad Exchanges share user impressions to facilitate RTB
  • More than 10% A&A domains view up to 90% of user impressions under

realistic conditions.

  • Due to RTB, impressions can leak to A&A domains even with blocking extensions
  • AdBlock Plus is not effective at all due to Acceptable Ads program
  • Disconnect performed the best in terms of protecting privacy
slide-93
SLIDE 93

Summary

  • We are the first to provide a model to study the impact of Real Time Bidding (RTB)
  • n user privacy.
  • Ad Exchanges share user impressions to facilitate RTB
  • More than 10% A&A domains view up to 90% of user impressions under

realistic conditions.

  • Due to RTB, impressions can leak to A&A domains even with blocking extensions
  • AdBlock Plus is not effective at all due to Acceptable Ads program
  • Disconnect performed the best in terms of protecting privacy

Questions?

ahmad@ccs.neu.edu

http://personalization.ccs.neu.edu/Projects/AdGraphs/

slide-94
SLIDE 94

Backup Slides

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SLIDE 95

Number of Exchanges for RTB-C

20

0.2 0.4 0.6 0.8 1 0.2 0.4 0.6 0.8 1 CDF Fraction of Impressions 5 10 20 30 50 100

slide-96
SLIDE 96

Model Validation — Per Publisher

21

50 100 150 200 250 300 O r i g i n a l R T B

  • R

R T B

  • C

C M # Nodes Activated

1 2 3 4 5 6 O r i g i n a l R T B

  • R

R T B

  • C

C M Tree Depth

slide-97
SLIDE 97

Inclusion Chain from DOM

22

slide-98
SLIDE 98

Inclusion Chain from DOM

22

<html> <body> <script src=“a1.com/cookie-match.js” </script> <!-- Tracking pixel inserted dynamically
 by cookie-match.js --> <img src=“a2.com/pixel.jpg” /> <iframe src=“a3.com/banner.html”> <script src=“a4.com/ad.js”> </script> </iframe> </body> </html>

DOM Tree for http://p.com/index.html

slide-99
SLIDE 99

Inclusion Chain from DOM

22

<html> <body> <script src=“a1.com/cookie-match.js” </script> <!-- Tracking pixel inserted dynamically
 by cookie-match.js --> <img src=“a2.com/pixel.jpg” /> <iframe src=“a3.com/banner.html”> <script src=“a4.com/ad.js”> </script> </iframe> </body> </html>

DOM Tree for http://p.com/index.html Inclusion of resources

slide-100
SLIDE 100

Inclusion Chain from DOM

22

<html> <body> <script src=“a1.com/cookie-match.js” </script> <!-- Tracking pixel inserted dynamically
 by cookie-match.js --> <img src=“a2.com/pixel.jpg” /> <iframe src=“a3.com/banner.html”> <script src=“a4.com/ad.js”> </script> </iframe> </body> </html>

DOM Tree for http://p.com/index.html

p

Inclusion of resources

slide-101
SLIDE 101

Inclusion Chain from DOM

22

<html> <body> <script src=“a1.com/cookie-match.js” </script> <!-- Tracking pixel inserted dynamically
 by cookie-match.js --> <img src=“a2.com/pixel.jpg” /> <iframe src=“a3.com/banner.html”> <script src=“a4.com/ad.js”> </script> </iframe> </body> </html>

DOM Tree for http://p.com/index.html

p

Inclusion of resources

slide-102
SLIDE 102

Inclusion Chain from DOM

22

<html> <body> <script src=“a1.com/cookie-match.js” </script> <!-- Tracking pixel inserted dynamically
 by cookie-match.js --> <img src=“a2.com/pixel.jpg” /> <iframe src=“a3.com/banner.html”> <script src=“a4.com/ad.js”> </script> </iframe> </body> </html>

DOM Tree for http://p.com/index.html

p a1

Inclusion of resources

slide-103
SLIDE 103

Inclusion Chain from DOM

22

<html> <body> <script src=“a1.com/cookie-match.js” </script> <!-- Tracking pixel inserted dynamically
 by cookie-match.js --> <img src=“a2.com/pixel.jpg” /> <iframe src=“a3.com/banner.html”> <script src=“a4.com/ad.js”> </script> </iframe> </body> </html>

DOM Tree for http://p.com/index.html

p a1

Inclusion of resources

slide-104
SLIDE 104

Inclusion Chain from DOM

22

<html> <body> <script src=“a1.com/cookie-match.js” </script> <!-- Tracking pixel inserted dynamically
 by cookie-match.js --> <img src=“a2.com/pixel.jpg” /> <iframe src=“a3.com/banner.html”> <script src=“a4.com/ad.js”> </script> </iframe> </body> </html>

DOM Tree for http://p.com/index.html

p a1

Inclusion of resources

a2

slide-105
SLIDE 105

Inclusion Chain from DOM

22

<html> <body> <script src=“a1.com/cookie-match.js” </script> <!-- Tracking pixel inserted dynamically
 by cookie-match.js --> <img src=“a2.com/pixel.jpg” /> <iframe src=“a3.com/banner.html”> <script src=“a4.com/ad.js”> </script> </iframe> </body> </html>

DOM Tree for http://p.com/index.html

p a1

Inclusion of resources

slide-106
SLIDE 106

Inclusion Chain from DOM

22

<html> <body> <script src=“a1.com/cookie-match.js” </script> <!-- Tracking pixel inserted dynamically
 by cookie-match.js --> <img src=“a2.com/pixel.jpg” /> <iframe src=“a3.com/banner.html”> <script src=“a4.com/ad.js”> </script> </iframe> </body> </html>

DOM Tree for http://p.com/index.html

p a1 a2

Inclusion of resources

slide-107
SLIDE 107

Inclusion Chain from DOM

22

<html> <body> <script src=“a1.com/cookie-match.js” </script> <!-- Tracking pixel inserted dynamically
 by cookie-match.js --> <img src=“a2.com/pixel.jpg” /> <iframe src=“a3.com/banner.html”> <script src=“a4.com/ad.js”> </script> </iframe> </body> </html>

DOM Tree for http://p.com/index.html

p a1 a2

Inclusion of resources

slide-108
SLIDE 108

Inclusion Chain from DOM

22

<html> <body> <script src=“a1.com/cookie-match.js” </script> <!-- Tracking pixel inserted dynamically
 by cookie-match.js --> <img src=“a2.com/pixel.jpg” /> <iframe src=“a3.com/banner.html”> <script src=“a4.com/ad.js”> </script> </iframe> </body> </html>

DOM Tree for http://p.com/index.html

p a1 a3 a2

Inclusion of resources

slide-109
SLIDE 109

Inclusion Chain from DOM

22

<html> <body> <script src=“a1.com/cookie-match.js” </script> <!-- Tracking pixel inserted dynamically
 by cookie-match.js --> <img src=“a2.com/pixel.jpg” /> <iframe src=“a3.com/banner.html”> <script src=“a4.com/ad.js”> </script> </iframe> </body> </html>

DOM Tree for http://p.com/index.html

p a1 a3 a2

Inclusion of resources

slide-110
SLIDE 110

Inclusion Chain from DOM

22

<html> <body> <script src=“a1.com/cookie-match.js” </script> <!-- Tracking pixel inserted dynamically
 by cookie-match.js --> <img src=“a2.com/pixel.jpg” /> <iframe src=“a3.com/banner.html”> <script src=“a4.com/ad.js”> </script> </iframe> </body> </html>

DOM Tree for http://p.com/index.html

p a1 a3 a2 a4

Inclusion of resources

slide-111
SLIDE 111

DOM -> Inclusion & Referer Graph

23

(a) DOM Tree for http://p.com/index.html

<html> <body> <script src=”a1.com/cookie-match.js”></script> <!-- Tracking pixel inserted dynamically by cookie-match.js --> <img src=”a2.com/pixel.jpg”/> <iframe src=”a3.com/banner.html”> <script src=”a4.com/ads.js”></script> </iframe> </body> </html>

(d) Referer Graph (c) Inclusion Graph a1 a2 a4 a1 a2 a4 a3 (b) Inclusion Tree

p.com/index.html a1.com/cookie-match.js a2.com/pixel.jpg a3.com/banner.html a4.com/ads.js

p a3 p Publisher A&A

slide-112
SLIDE 112

Comparison with Random Model

24

0.2 0.4 0.6 0.8 1

  • 0.3
  • 0.2
  • 0.1

0.1 0.2 0.3 0.4 0.5 0.6 0.7 CDF Impression Fraction Difference (Burken - Random) Per A&A Node RTB Relaxed-Top 10% RTB Relaxed-Disconnect RTB Relaxed-Ghostery RTB Relaxed-Random 30% RTB Relaxed-No Blocking