11 class 2012 socialnetworks

7/31/2019 11 CLASS 2012 SocialNetworks

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Systems challenges inonline social media

Alan Mislove

College of Computer and Information ScienceNortheastern University

February 22nd, 2012, Networks Class


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22.02.12 Networks Class Alan Mislove

Social networking sites (Web 2.0)

Popular way toconnect and share contentPhotos, videos, blogs, profiles, news, status...

MySpace (275 M), Facebook (500 M)

Growing exponentially

Incredible amounts of content being sharedFacebook (7.5 B photos/month)

YouTube (48 hours of video/min)

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0

125

250

375

500

04 06 08 10FacebookU

sers(millions)


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Whats new in Web 2.0?

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Web 2.0Web 1.0


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My groups research

Thesis: OSNs (Web 2.0) fundamentally different from Web 1.0

Introducing new and unforeseen challengesNeed new approaches to address these challenges

Leveraging social networks results in better systemsDue to the increasing integration of systems and social networks

But, must be backed by measurement and analysis

My groups research is motivated by effects of this changeWill give three examples today

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Alan Mislove12.06.10 University of Massachusetts, Boston

Effect 1:

Changing patterns of content creation + exchange

submitted tousenix12

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22.02.12 Networks Class Alan Mislove 6

Pre-2005 Web (a.k.a. Web 1.0)

Telecom ItaliaTelvia

Fastweb NGI


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Difference 1: Content popularity

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Fraction of documents(ranked from most to least popular)

Fractionof

requests

facebook photos [2]

classic web [1]

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[1] Breslau et al., INFOCOM, 1999, [2] Mislove et al., WSDM, 2010

even distribution


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Implication: Caches less effective

Popularity distribution much more evenObjects have more narrow scope

In classic Web:Caching top 10% serves between 55% [1] and 95% [2] of requests

Success of CDNs, web caches, ...

In online social media:Caching top 10% would only serve 27% [3] of requests

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[1] Breslau et al., INFOCOM, 1999, [2] Arlitt et al. IEEE Network, 2000, [3] Mislove et al., WSDM, 2010


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Difference 2: Content generation

Telecom ItaliaTelvia

Fastweb NGI


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Implication: Workload change

Significant content creation at networks edgeEase of digital content creation (photos, video)

Ubiquity of Internet access (cell phone, iPad)

In classic Web:Workload was center-to-edge

Caching, CDNs take load off origin server

In online social media:Workload is edge-to-edge

Significant geographic locality

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How is OSN content being delivered?

Web 1.0 centralized architectures dominateAkamai, Limelight, Clearway, ...

Facebook serves much of its own content

Mismatch between infrastructure, workload

Workload is naturally decentralizedEvery Facebook upload goes via CA

Can we build a workload-matching distribution system?Avoid unnecessary, expensive transfers

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WebCloud: Decentralized delivery

First step towards decentralized Web content deliveryChallenge: Web doesnt support decentralization

Browsers distinct from Web servers

Use novel techniques to allow browser to serve contentNo client-side changes

Users help serve content they upload

Result: Scalable, workload-matching architecture

Next: Brief technical discussion

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WebCloud design overview

Goal: Move towards more decentralized content exchangeKeep content exchange at the edge

Want to make it work with todays sites, browsers

Reason: Users wont install anythingCant require users to do anything different

Idea: Introduce a middlebox to allow browsers to communicate

To build WebCloud, need to makeClient-side changes

Deploy middleboxes

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Client-side changes

Want to turn web browser into web serverImplement WebCloud in Javascript

Add it to the sites pages

Use LocalStorage to storage browsed contentPersistent cache, up to 5MB/site

Easily programmatically accessed

Treated like LRU cache

Use WebSockets/XHR to communicate with middleboxAllows bi-directional communication

Onlineclient is always connected to middlebox

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+


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Middleboxes

Add redirector proxies in each ISPLike Akamai proxy, but doesnt store any content

Maintains open connect to online web visitors

Run by OSN provider

Clients connect to proxyInform proxy of locally stored content

Clients request content from proxyProxy checks for other local clientsFound: fetches content, forwards to requestor

Not found: fetches content from origin site

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Redirector

proxy


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Putting it all together

Overall, WebCloud serves as a distributed cache

Use content-hashes to ensure integrity

Privacy implicationsk-anonymity for viewers

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Redirector

proxy

Client A

Client B

Internet


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WebCloud applied to real-world site

Top-50 U.S. web siteSimulation based on Akamai logs

Would dramatically reduce bandwidth requiredSavings for both site and ISP

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Time(one week)

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andwidth

(MB/s)

76% reduction in 95th percentile bandwidth


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Summary

Beginnings of shift in patterns of content creation + exchangePatterns changing from center to edge to edge to edge

Less biased popularity distribution

But, still using centralized delivery architectures

WebCloud: Step towards decentralized Web content deliveryUsers help serve content they create

Implemented using existing browser features; no client changes

Evaluation demonstrated practicality, efficacy

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Effect 2:

Changing meaning of accounts/identity

nsdi11

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Sybils

Free accounts with privileges leading to Sybil attacks [IPTPS 2002]Single person creates many accounts

Why?

Natural: Gain extra privilegesIncentives set up to encourage this

Examples in the wildMaze [ICDCS 2007]

Digg [NSDI 2009]

TripAdvisor [NYT, 10/2011]

Facebook, Gmail [me, others]

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Example: Online marketplaces

Among most successful Web siteseBay alone: $62 b in 2010

But, known to suffer from fraud

Auctions

Marketplace


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Identities and reputations

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$40

$25

$90

$2

$5

$1

$300

$90

$50

Feedback profile

$300

$90 $90

Significant monetary lossesRecent arrest of user who stole $717 k from 5,000 users

Used >250 accounts


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Bazaar: A new approach

New approach to strengthening user reputations

Leverages an (existing) risk network

Focuses on protecting buyers from malicious sellers

Works in conjunction with existing marketplaceAssumes same feedback system as today

No additional monetary cost

No strong identities

Insight: Successful transactions represent shared riskBuyer and seller more likely to enter into future transactions

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Bazaars risk network

Successful transaction two identities linked

Weighted by amount of transaction

Risk network automatically generatedUsers need not even know about it

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$5

$25

$1

$7

$45

$4$10

$3

$50$10


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Estimating risk

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Bazaar calculates max-flow between buyer and sellerIf max-flow lower than potential transaction, flag as fraudulent

$5

$300

$4000

$50

$200

$100

Max-flow: $5

Buyer

Seller


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Summary

Increasing trend of online services with free accountsOpens new vector for attack

Focused on reputation manipulation in online marketplacesBazaar: A new approach to strengthening reputations

Evaluated on 10 m auctions from eBay UKWould have prevented 164 k of negative feedback

Only in five categories over 90 days

Currently looking to apply techniques to other domains

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Effect 3:

Changing requirements of end users

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16.10.2009 CCIS/COE Retreat Alan Mislove 29

Privacy on OSNs

Privacy is a significant issue on OSNs

Received recent press, research attention

What is underlying privacy debate?

1. Sites control personal information of millions of users

2. Users are expected to manage their privacy

5,830 word privacy policyOver 100 different settings

Default is open-to-the-world (over 800 million users)


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16.10.2009 CCIS/COE Retreat Alan Mislove

A fundamental shift for users

Prior to OSNs

Users were largely content consumers

Now, with sites like Facebook

Users expected to be content creators and managersMust enumerate who is able to access every uploaded content

Avg. 130 friends, 90 pieces of content/month...

Whats the extent of privacy problem?

So far, most studies anecdotal

Can we quantify the extent of the privacy problem on Facebook?

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Facebook privacy model

Consider Facebook-supported content:

Photos, Videos, Statuses, Links and Notes

Five sharing granularities:

Only Me (Me)Some Friends (SF)

All Friends (AF)

Friends of Friends (FoF)

Everyone (All)

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Measuring desired and actual settings

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Design a Facebook survey application

Collects actual setting for all content

Selects up to 10 photos

Asks user about desired privacy setting

Recruit using Amazon Mechanical Turk

Total of 200 Facebook users

Pay them each $1

116,553 actual settings

1,675 desired settings

Study was conducted under Northeastern IRB protocol #10-10-04


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What are the existing privacy settings?

36% of all content shared with the default (visible to all users)

Photos have the most privacy-conscious settings

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Photo Video Status Link Note

Fraction

of

Content

Only Me

Some Friends

All Friends

Friends of Friends

Everyone

Default


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What about photos with modified settings?

Settings match only for 39% of privacy-modified photosEven when user has explicitly changed setting

Take-away: Not just poor defaults

Users have significant trouble managing their privacy35

Actual

Setting

De ired Setting

Me SF AF FoF All

Me

SF

AF

FoF

All

2 6 4 0 4

2 12 29 8 11

40 8 237 40 69 218 (28%)

39 17 148 45 47

0 0 0 0 0

Total 54 (33% 296 (39%)

Additional 768 photos with non-default privacy settings


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Can we improve sharing mechanisms?

Can we provide better management tools?

Ease users role as content manager

Idea: Leverage the structure of the social network

Create privacy groups from users friends

Update the groups as the user forms or breaks friendships

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Automatically detecting friendlists

Friendlists: Facebook feature similar to Google+ Circles

Ground truth; Meaningful groupings of users for privacy

Collected 233 friendlists from our 200 AMT users

Do friendlists correspond with the social network?Normalized conductance [WSDM10] rates the quality of community

Strongly positive values indicate significant community structure

Results on 233 friendlists:Over 48% friendlists correspond to strong communities

May be able to be inferred from social network

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Summary

Privacy an important issue on OSNs

But, to date, no quantification of privacy problem

Develop methodology to measure actual, desired privacy settings

Deployed to 200 Facebook users from AMT

Findings:

36% of all content shared with the default settings

Privacy settings match expectations less than 40% of the timeEven when users has already modified setting

But, potential to aid users by providing better mechanisms

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Conclusion

Social networks and computer systems increasingly integratedNew way of organizing information

Leading to new opportunities, challenges

My groups goal: Leverage social networks in systems design

WebCloud: Addresses challenges with emerging workloads

Bazaar: Addresses challenges with free accounts

Privacy: Addresses difference between privacy perception andreality

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Questions?

Work done in collaboration with

Ben Adams (MPI-I), Bobby Bhattacharjee (University of Maryland), Meeyoung Cha (KAIST),

Peter Druschel (MPI-SWS), Krishna P. Gummadi (MPI-SWS),

Andreas Haeberlen (University of Pennsylvania), Ancsa Hannk (Northeastern University),

Jonathan Katz (University of Maryland), Hema Swetha Koppula (Yahoo Research India),

Sune Lehmann (TU Copenhagen), Yabing Liu (Northeastern University),

Arash Molavi (Northeastern University), Jukka-Pekka Onnela (Harvard University),

Ansley Post (Google), J. Niels Rosenquist (Harvard Medical School),

Neil Spring (University of Maryland), Ravi Sundaram (Northeastern University),

Malveeka Tewari (University of California, San Diego), Bimal Viswanath (MPI-SWS),

Liang Zhang (Northeastern University), Fangfei Zhou (Northeastern University)

11 class 2012 socialnetworks

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