If you are not paying for it, you are the product: How much do advertisers pay for your personal data?

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by  Panagiotis Papadopoulos

If you are not paying for it, you are the product:
How much do advertisers pay for your personal data?
PanagiotisPapadopoulos1,NicolasKourtellis2,PabloRodriguezRodriguez3,NikolaosLaoutaris2
1FORTH-ICS,Greece,2TelefonicaI+D,Spain,3TelefonicaAlpha,Spain
7 [email protected],2,3{name.lastname}@telefonica.com
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0
2
Abstract toincreasetheirmarketshare,theyneedtodelivermore
r
a effective and highly targeted advertisements. A way to
Online advertising is progressively moving towards a
M achievethisisthroughprogrammaticinstantaneousauc-
programmatic model in which ads are matched to ac-
tions. An important enabler for this kind of auctions is
tualinterestsofindividualscollectedastheynavigatethe
3 the Real-Time Bidding (RTB) protocol for transacting
web. Letting the huge debate around privacy aside, a
digital display ads in real time. RTB has been growing
very important question in this area, for which very lit-
] withanannualrateof128%[81],andcurrentlyaccounts
T tle is known, is: How much do advertisers pay for an
for 74% of programmatically purchased advertising. In
G individual’spersonaldata? USaloneitcreatedarevenueof$8.7billionin2016[7].
Inthisstudy, wedevelopafirstofitskindmethodol-
. Consequently, the collection of user personal data
s ogyforcomputingexactlythat–thepricepaidforone’s
c privacy – and we do that in real time. Our approach is has become more aggressive and sometimes evenintru-
[ sive [31, 35], raising a huge public debate around the
basedontappingontheRealTimeBidding(RTB)proto-
2 coltocollectcleartextandencryptedpricesforwinning tradeoffsbetween(i)innovationinadvertisingandmar-
keting, and (ii) basic civil rights regarding privacy and
v bids paid by advertisers in order to place targeted ads.
personal data protection [57, 53]. These increasing pri-
8 Ourmaintechnicalcontributionisamethodfortallying
vacy concerns, drew the attention of a significant body
5 winningbidsevenwhentheyareencrypted. Weachieve
of research, which studied users’ privacy loss in con-
0 thisbytrainingamodelusingasgroundtruthpricesob-
junction to existing user tracking techniques [1, 22, 61,
7 tained by running our own “probe” ad-campaigns. We
17,54],andproposedvariousdefensemechanismstothe
0 implement our methodology through a browser addon
. andaback-endserverthatprovidesitwithfreshmodels users [60, 65, 66]. However, with all this pervasive ad-
1 relatedusertrackinganddatacollection,anoutstanding
for encrypted bids. We validate our methodology using
0 questionstillremainsunaddressed:Whatisthemonetary
7 a one year long trace of 1600 mobile users and demon-
cost of the users’ collected private data? Or, in other
1 stratethatitcanestimateauser’sworthwithmorethan
terms,howmuchdoadvertiserspayforusers’privacy?
: 82%accuracy.
v Despitetheimportanceofthesequestions,itissurpris-
i ing how little is known about them There exist several
X
1 Introduction reportsabouttheaveragerevenueperuser(ARPU)from
r online advertising [32, 13, 68], but ARPU, as its name
a
Intoday’sdata-driveneconomy,theamountofuserdata suggests, is just an average. It can be calculated by di-
anITcompanyholdshasadirectandnon-trivialcontri- vidingthetotalrevenueofacompanybythenumberof
bution to its overall market valuation [74]. Not surpris- itsmonthlyactiveusers. Computingtherevenueperin-
inglytherefore, companiespursuewithfullstrengththe dividualuserisacompletelydifferentbusinessforwhich
acquisitionofevermoredetailedpersonaldata,eitherby verylittleworkisavailable.
collectingthemwhileprovidingfreeservices[3,34],or In particular, the FDVT [14] browser addon can es-
by buying them from others [84, 10, 51]. Conservative timate the value of an individual user for Facebook, by
estimatesputthemarketofpersonaldatatohundredsof tapping on the platform’s ad-planner. Also, similarly
billiondollars[23,86,18]. to us, a method in [63] leverages the RTB protocol and
Digitaladvertising isoneof themain meansofmon- specificallyitsfinalstage,wherethewinningbidder(ad-
etisinguserdata. Itgrewto$194.6billionin2016[76] vertiser) gets notified about the auction’s charge price
ofwhich$108billionwereduetomobileadvertising. In perdeliveredimpression. Thesechargepriceswereini-
fact,moreandmorecompaniesrushtoparticipateinthis tiallytransmittedincleartextand[63]focusedsolelyon
rapidly growing advertising business either as advertis- them. However, more and more advertising companies
ers, ad-networks, demand-side platforms, data manage- useencryptiontoreducetheriskoftampering,falsifica-
mentplatforms,oralloftheabove. Forthesecompanies tion or copying from adversary competitors. This trend
1
renders that method inapplicable for the current and fu-
tureadecosystem,whosemajorityofcompanieswillde- (1) User visits SSP DSP
ploychargepriceencryption. Incontrasttotheseworks, User pwubeblisphaegre’s (2) Ad request ADX (3) UBisde rr einqfuoest,
ourpresentmethodtakesintoaccountallthewebactiv-
ity of a user (not only on Facebook [14]), and all RTB Ad-
slot
traffic,i.e.,bothcleartextandencryptedprices. (6) Winner’s impression (5) Place bids
Inthispaper,wedevelopandevaluateafirstofitskind delivery
methodology for enabling end-users to estimate in real (7) “charge price” and
“impression rendered” notification
timetheiractualcostforadvertisers,evenwhenthelatter
encrypt the prices they pay. Implemented as a browser
(4) Retrieve user interests,
extension,ourmethodcantallywinningbidsfortheads User Tracking geolocation, behavior
Data Hub:
showntoauseranddisplaytheresultingamountasshe DMP, Data Broker,
moves from site to site in real time. To summarize, in etc.
thispaper,wemakethefollowingmaincontributions: Figure1:HighleveloverviewoftheRTBecosystem.Severalentities
interactwitheachother, exchanginguser’spersonaldatabeforeitis
1. We propose the first to our knowledge holistic finallyconvertedtomoney.
methodology to calculate the overall cost of a user
browserofauser. Section7putsallthepiecestogether
fortheRTBadecosystem,usingbothencryptedand
and presents results on the overall advertising cost for
cleartext price notifications from RTB-based auc-
displaying ads to users. Section 8 covers related work
tions.
andSection9concludesthepaper.
2. We study the feasibility and efficiency of our pro-
posed method by analyzing a year-long weblog
2 BackgroundonRTB
of 1600 real mobile users. Additionally, we de-
signandperformanaffordable(afewhundreddol-
lars cost) 2-phase real world ad-campaign target- RTB accounts for 74% of programmatically purchased
ingad-exchangesdeliveringcleartextandencrypted advertising,reachingatotalrevenueof$8.7billioninthe
prices in order to enhance the real-users’ extracted US[7].Itallowsadvertiserstoevaluatethecollecteddata
prices. We show that even with a handful of fea- ofagivenuseratreal-timeandbidforanad-slotinthe
turesextractedfromthead-campaign,ourmethod- user’sdisplay. Next,webrieflycoverthemostimportant
ology achieves an accuracy >82%. The resulting aspectsofRTBauctions,thekeyentitiesinvolved(§2.1),
ARPUis∼55%higherthanthatcomputedbasedon andhowtheyarerelevanttoourstudy(§2.2).
cleartextRTBpricesalone. Ourfindingschallenge
the related work’s basic assumption [63] that en-
2.1 Thekeyplayers
crypted and plain text prices are similar (we found
encrypted prices to be ∼1.7× higher). Finally, we As can be seen in Figure 1, the key roles of the RTB
validate our methodology by comparing our aver- ecosystem include the Advertiser, Publisher, DSP, Ad-
age estimated user cost with the reported per user exchange,andSSP,whichinteractwitheachotherinsev-
revenueofmajoradvertisingcompanies. eralways. Notethatitisverycommonforsome(large)
companiestoplaysimultaneouslydifferentrolesevenin-
3. We implemented a prototype of our approach,
side the same auction (e.g. Google’s DoubleClick Bid
where the users, by using our Chrome browser ex-
Manager[30]andDoubleClickforPublishers[25].
tension, can estimate in real-time, in a privacy-
Publisher: (e.g., CNN.com) is the owner of a website,
preservingfashionontheclientside,theoverallcost
whereusersbrowseforcontentandreceiveads(step1).
of their exposed private information for the adver-
Eachtimeauservisitsthewebsite,anauctiontakesplace
tisers. In addition, they can also contribute anony-
foreachavailableadslot. Theadimpressionofthewin-
mouslytheirimpressionchargepricestoacentral-
ningadvertiserisfinallydisplayedineachauctionedslot
izedplatformforfurtherresearch.
ofthewebsite.
Therestofthepaperisorganizedasfollows:Section2 Advertiser: isthebuyerofawebsite’sadslots. Thead-
summarisesvariouskeyconceptsandentitiesoftheRTB vertiser creates ad campaigns and defines the audience
ecosystem. Section 3 presents the main challenge and thathastobetargetedaccordingtohismarketingobjec-
thus motivation for our work – the increasing popular- tives, budgets, strategies, etc. In each auction, the one
ity of encrypted price notifications in RTB. Section 4 with the highest bid wins the ad slot and places its im-
provides a high-level overview of our novel methodol- pressiononthescreenofthewebsite’svisitor.
ogy and our price modeling engine. Section 5 presents Supply-Side Platform (SSP): is an agency platform,
an analysis of a year-long weblog dataset of more than which enables publishers to manage their inventory of
1500 users that was used to bootstrap our modeling of available ad slots and their pricing, allocate ad impres-
encrypted prices. Section 6 presents in detail the effort sions from different channels (e.g. RTB or backfill in
to model RTB charge prices by executing probing ad- caseofunsoldinventory[48])andreceiverevenue. SSP
campaigns. Thesecampaignsprovideground-truthdata, is also responsible for interfacing the publisher’s side
which then is used to train a machine learning classi- tomultiplead-exchanges(step2)andaggregate/manage
fierthatcanestimateencryptedpricesinreal-timeatthe publisher’s connections with multiple ad networks and
2
buyers. In addition, by using web beacons and cookie WinningPriceNotificationURLs
synchronization, SSPs perform user tracking in order (A)cpp.imp.mpx.mopub.com/imp?addomain=amazon.es&
adscreativeid=ID&bidprice=0.99&bidderid=ID&...
to better configure their ad slots’ pricing and achieve biddername=..&chargeprice=0.95&country=..&...
as many re-targeting ads as possible and thus higher currency=USD&latency=0.116&mopubid=ID&pubname=..
bids [45]. Popular vendors selling SSP technology in-
(B)tags.mathtag.com/notify/js?exch=ruc&price=B6A3F3C19F50C7FD&...
clude:OpenX,PubMatic,RubiconProject,RightMedia. 3pck=http%3A%2F%2Fbeacon-eu2.rubiconproject.com%2F
beacon%2Ft%2Fce48666c-6eb4-46db-b0e9-6f4155eb557d%2F
Ad-exchange(ADX):isadigital,real-timemarketplace
that, similar to a stock exchange, enables advertisers (C)adserver-ir-p.mythings.com/ads/admainrtb.aspx?googid=ID&
width=300&height=250&...&cmpid=ID&gid=ID&mcpm=60&...
andpublisherstobuyandselladvertisingspacethrough
rtbwinprice=VLwbGQANXi4K21KFAAAm2ziqnOSO5oNkFuuJw&..
RTB-basedauctions. ADXisresponsibleforhostingan
Table1: Examplesof(A)cleartextand(B,C)encryptedRTBprice
RTB-based auction and distribute the ad requests along notifications.“ID”istypicallyahexadecimalnumber.
withuserinformation(i.e.browsinghistory,demograph-
ics, location, cookie-related info) may own, among all
tomerunderstanding,(ii)totheSSPsforretrievingmore
theinterestedauctionparticipants(step3).
re-targetedadsand(iii)totheDSPsforfeedingtheirbid
Typically these auctions follow the second higher decisionengines. Furthermore,qualityscoresareimpar-
pricemodel(i.e. Vickreyauctions)[80],thus,thecharge tiallyassignedtousers’privatedatabasedonthesuccess
priceforthewinneroftheslotisthesecondhighestsub- oftheadcampaignstheywereused,thusdrivingthebid
mitted. Itsmainadvantageisthatitforcesallbiddersto prices of future ad-campaigns. Notable Data Hubs in-
havetheirbidstrulyreflectonwhattheythinkthevalue clude: Turn,Adobe,Krux,Bluekai,Lotame.
of the specific ad slot should be based on the conjoined
user info. After the auction, the winning impression is
2.2 RTBpricenotificationchannel
served to the user’s display within 100 ms of the ini-
tiating call (step 6) and the winning bidder is notified
WhenanADXselectsthewinningbidofanauction,the
about the final charge price. Popular ad-exchanges in-
corresponding bidder must be notified about its win to
clude: DoubleClick,MoPub,andOpenX.
log the successful entry and the price to be paid to the
Demand-Side Platform (DSP): is an agency platform,
ADX. One could implemented this notification in two
which employs decision engines with sophisticated au-
ways: (i)withaserver-to-servermessagebetweenADX
dience targeting and optimization algorithms aiming to
andDSP,(ii)withanotificationmessageconjoinedwith
help advertisers buy the best-matched ad slots from
the price, passed through the user’s browser as a call-
ADXs in a simple, convenient and unified way. DSPs
backtotheDSP.
retrieveandprocessuserdatafromseveralsources(step
The first option is straightforward and tamper-proof;
4)suchasADXs,DataHubs,etc. Theresultofthispro-
no one can modify or block these messages, allowing
cessing is translated to a decision in practice: Does it
companies to ensure that their logs are fully synced at
worthtobidforanadslotforthisuser,andhowmuch?
anytime. Inaddition, DSPscanhideinformationabout
If the visitor’s profile matches the audience the adver-
the transactions, the purchased ad-slots and the prices
tiserhasfocusedhisadcampaignon,theDSPwillsub-
paid from the prying eyes of competitors. However,
mit to the ADX the impression and a bid in CPM (cost
DSPsdonothaveanyindicationofthedeliveryofeach
per1000impressions[40],typicallyinUSDorEuro)on
ad,inordertoinformtheircampaignsandbudget.
behalfoftheadvertiser(step5). PopularDSPsinclude:
Instead,thesecondoptionnotonlycanensuretheDSP
MediaMath, Criteo, DoubleClick, AppNexus and Invite
that the winning impression was indeed delivered (the
Media.
callbackisfiredsoonaftertheimpressionisrenderedon
UserDataHuborDataExchangePlatform(DXP):is
theuser’sdevice),butalsogivestheopportunitytodropa
acentralizeddatawarehousesuchasaDataManagement
cookieontheuser’sdevice.Therefore,thesecondoption
Platform (DMP) [47, 8] or a Data Broker [51] which
isthedominantoneinthecurrentmarket: theADXpig-
aggregates, cleans, analyzes and maintains user private
gybacks a notification URL (nURL) in the ad-response,
data such as demographics, device fingerprints, inter-
whichdeliverstotheuserthewinningimpressionandthe
ests, online and offline contextual and behavioral infor-
ad (steps 6 and 7 in Figure 1). This nURL includes ba-
mation[43,42].Theseuserdataaretypicallyaggregated
sicallythewinningDSP’sdomain, thechargeprice, the
intwoformats: 1)afull,audienceuserprofileforoffline
impressionID,theauctionIDandotherrelevantlogistics
analyticsanddatamining,2)arun-timeuserprofile,op-
(see Table 1 for some examples). In this present work,
timizedforreal-timerequestssuchasRTBqueriesfrom
westudysuchnURLsandthepricesembeddedinthem,
DSPs, before submitting their bids to ADXs [19, 44]
as well as how they associate with the users’ browsing
(step4).
behaviorandotherpersonalinformation.
Such user profiles are sold to ad entities [4] because
theyincreasethevalueofanRTBinventorybyenabling
amorebehavioral-targetedadvertising(2.7×moreeffec- 3 UserDataCostfromRTBPrices
tivethannon-targetedadvertising[5,83]). Infact,Data
Hubs are considered the core component of the digital A user’s personal data and preferences are at the core
ad-ecosystemastheyperformtheattributionandlabeling ofcontemporarydigitaladvertising. AdTechcompanies
of users’ data and create groups, namely audience seg- spend a lot of effort to aggregate such data, build user
ments,whichareuseful(i)tothepublishersfortheircus- profiles and ad-audiences, and sell them to 3rd parties.
3
Notation Definition
100 % 100%
ADX-DSP pairs24680000 %%%% Cumulative portionof cleartext prices24680000%%%% VCSFSCiuiu⊆,uE,FSuiEu TSSVCoueeottcmrateolofofrcefcooalcseftltuaeforreaetfaresttuxeusstxre,eetler,seneucfcntoercrydrpayttpopetrderiedcpperprinercisUeceeRnnsUtLonRfiULuRsseLorfiuuseru
0 % 1 2 3 4 Mo5nth6 of 7the 8yea9r 101112 0% 33.551%0.749%.42%6.91%6.46%4.45%4.14%3.87%3.54%2.93%2.52%2.40%2.36%2.00%1.97%1.68%1.06% ESe(Si) EofstfiematauteredseSncorfypprtiecdepnrUicReLbaisedonvector
encrypted cleartext RTB share of top ad-entities
Table2:Summaryofnotations.
Figure2:Percentageofencrypted Figure 3: Cumulative portion of
and cleartext pairs of ADX-DSP cleartextpricesfoundwithrespect
throughtime(2015). to advertising entities’ portion of Inaddition,wefoundthatthemobileadvertisingenti-
RTBpricesfound.
ties with the larger RTB shares, deliver the highest por-
tion of cleartext prices as well (Figure 3). For exam-
Themainmotivatingquestionofthisworkishowmuch
ple, MoPub and Adnxs, the two leading ad-entities in
advertiserspayforuser’spersonaldatawhiledelivering
our dataset, are responsible for 33.55% and 10.74% of
adstothem.
theoverallRTBadsdetected,respectively(x-axis).They
arealsoresponsiblefor45.40%and5.45%forthecleart-
3.1 Encryptedvs. cleartextprices extpricesdetected,respectively(cumulativelyiny-axis).
Thismeansthatifthesetwocompaniesfliptheirstrategy
Nowadays,chargepricesinnURLscanbeeitherinclear- fromcleartexttoencrypted,theoverallratioofencrypted
textorencryptedform(seeexamplesinTable1). Clear- and cleartext prices will significantly change overnight,
text prices captured at the user’s browser can be easily andanexternalobserverortheinvolveduserwilllosea
talliedtoestimatethetotalcleartextcost. Thesamedoes significantportion(∼51%)ofinformation.
not apply for the encrypted prices though. The 28-byte Given these trends in mobile and desktop, we expect
encryptionscheme,thatisusedinmostofthecases,can- that in the near future RTB auctions will dominate, and
notbeeasilybroken[28]. many of the ad-entities will use encryption to deliver
Relatedstudies[63]assumedthatencryptedpricesfol- their charge prices. Our methodology anticipates these
low the same distribution as cleartext. Indeed, onemay trendsandpromotesbettertransparencyinonlineadver-
argue that the price encryption is to avoid tampering of tising and usage of user personal data. This method-
reported prices, and thus encrypted prices probably fol- ology allows end-users to accurately estimate on their
low the cleartext price distribution. This could be the browser,atreal-time,theiraveragead-relatedcumulative
caseforaportionofthem. However, itcanalsobethat cost,evenwhentheadchargepricesareencrypted.
an encrypted price may be a sign of a higher value that
the DSP wants to hide, for several reasons: aggressive
re-targeting because of user’s previous incomplete pur-
4 Methodology
chases, targeting users with higher spending habits, or
userswithspecializedneeds(e.g.,sensitiveproducts,ex-
pensivedrugs, etc.). Therefore, an encryptedpricemay In this section, we describe our proposed methodology,
be used by a DSP to reduce transparency over its bid- withwhichauserucanestimateinrealtimetheaccumu-
ding strategies, or possible special knowledge it may latedcostV fortheadsshewasdeliveredwhilebrows-
u
have about a specific user, thus preventing an external ing the web (§ 4.1). Table 2 presents the notations we
observerorcompetitorfromassessingitsbiddingmeth- use through this section. Following this methodology,
odsandad-campaigns. we design our system based on two main components:
In our study, we do not adopt the above assumption (i) a remote Price Modeling Engine (§ 4.2) and (ii) a
and we handle encrypted prices separately. This allows browserextension,namelyYourAdValue(§4.3).Figure4
ustoaccountforanypotentialdifferenceintheirdistri- presentsahighleveloverviewofourproposedmethod-
butionfromcleartextprices. ology.
3.2 Encryptedpricesontherise
4.1 Overallcostoftheuser’sdata
Encryption is a regular practice in desktop RTB ads
(∼68% as reported in [62] with major supporters being The overall ad-cost of the user for time period T is the
DoubleClick,RubiconProjectandOpenX).Byanalyzing sumofchargepricestheadvertisershavepaidaftereval-
a weblog of 1600 real mobile users (see Section 5), we uatingherpersonaldatatheyhavecollectedanddeliver-
detected a smaller portion in mobile RTB ads (∼26%). ing ads to her device. Specifically, this overall value is
However, we found that the percentage of ADX-DSP thesumofbothhercleartextC (T)andencryptedE (T)
u u
pairsusingencryptedpricenURLswassteadilyincreas- pricesandcanbestatedas:
ing through time (Figure 2), which means that more
and more mobile advertising entities have started using
nURLswithencryptedprices. V (T)=C (T)+E (T) (1)
u u u
4
“probing ad-campaigns” to collect ground truth of real
Price Modeling Engine Dataset from ad-campaigns & contributions
with cleartext& encryptedprices chargepricesfrombothencryptedandcleartextformats.
Ad-campaign, configuration
Anonymously contribute setups, ADXs with cleartext Considering all possible metadata available, i.e. auc-
clmeaerttaedxatt&a eennccoryupntteedrepdri cine s Dimreednusciotinoanlity &in teenrcvraylsptedprices, regular tions’ metadata and users’ personal data, they amount
-⊆1 Modeling encryptedprices to hundreds of data points per price to be fed into the
modelingengine,increasingexcessivelythecomputation
M(S) cost. Additionally, if all data points were to be used in
theprobingad-campaigns,theywouldrendersuchcam-
User !"encryptedprices /(-) ,-!"(-) Cumulative value of user u paignsinfeasible, money-wise. Inordertoruneffective
for time period T and efficient ad-campaigns, and enable the price model
� �
noRtTifBic cahtiaorng ef ipltreircien g #" cleartext prices %(')=*� #"+*� ,-!" tpoertfroarimnswdiitmhoeuntsihoingahlitcyormedpuuctatitoionnofotvheerheexatrda,cttehdemPMetaE-
& metadata collection Web Browser + YourAdValue
data (F) into a subset S ⊆ F of core features that de-
scribewellthevariabilityofcleartextprices,andconse-
Figure4:High-leveloverviewofourmethod.Theuserdeploysaweb
browserextensiononherdevice,whichcalculatesatreal-timethetotal quentlyofencryptedprices. Thisdimensionalityreduc-
costpaidforherbyadvertisers.Incaseofencryptedprices,itappliesa tionmakestheprobingad-campaignsfeasiblebyreduc-
decisiontreemodelderivedfromthePMEandprobingad-campaigns. ingbymanyordersofmagnitudetheneededfeaturesof
each testing setup, and effectively the number of setups
The cleartext prices of a user can be aggregated in a
tobetested(seeSection 6).
straightforward fashion, thus producing the ad-cost for
useruoversuchprices: Using the collected ground truth of encrypted prices
from ad-campaigns, the PME trains a machine learning
C (T)=∑c, where i∈SC (T) (2) modelMtoinferencryptedpricesbasedontheirassoci-
u i u
atedsubsetoffeaturesS. Then,eachusercanapplythe
i
modelM (intheformofadecisiontree)locallyontheir
On the other hand, the calculation of the aggregated browsertoestimateeachofherencryptedchargeprices
Eu(T) of the encrypted prices for the same user is not basedonthematchingmetadataS.
easy. Theactualpricevaluesei arehiddenandtherefore In case the availability of cleartext prices is limited,
need to be estimated. To achieve that, we leverage the thereductionsteptoidentifyimportantfeaturescouldbe
metadata of each charge price in the user’s set SEu(T) hindered, but not obstructed. To mitigate this, the PME
ofencryptedpricenotifications. Suchmetadatamayin- can run more probing ad-campaigns to cover extra fea-
clude: time of day, day of week, size of ad, DSP/ADX tures found in users’ anonymous contributions, or that
involved, location, type of device, associated IAB, type are available in professional ad-campaign planners (as
ofOS,user’sinterests,etc. Allthesemetadatacancom- in FDVT [14]). Then, the most important features can
pose a feature vector Fi that characterize the value of a beselectedbasedontheircontributiontomodeltheen-
specificchargepriceei,innURLi. cryptedpricesextractedfromthesecampaigns.
In order to estimate each encrypted notification price
i, webuiltamachinelearningmodel, whichreceivesas
inputthefeaturevectorFi(orasubsetSi⊆Fi),extracted 4.3 YourAdValuebrowserextension
from the nURLi, and estimates a charge price ESe(S)
i
for the encrypted price ei. Then, we can aggregate the YourAdValue is a browser extension responsible for
estimatedencryptedpricesforuseru: monitoringtheusers’nURLsandcalculatinglocallythe
cumulativecostoftheuseratrealtime. Toachievethis,
Eu(T)=∑ESe(Si), where i∈SEu(T) (3) it filters nURLs from the user’s web traffic and extracts
i (i)theRTBauction’schargeprices(bothencryptedand
cleartext), and (ii) metadata from each specific auction
(e.g. timeofday,dayofweek,sizeofad,involvedDSP
4.2 PriceModelingEngine
and ADX, etc.) along with the personal data the user
Price Modeling Engine (PME), the main component of leaks while using online services (e.g. location, type of
ourapproach, isacentralizedrepositoryresponsiblefor deviceandbrowser,typeofOS,browsinghistory,etc.).
the estimation of encrypted prices. In order to achieve Aswementionedearlier,cleartextpricescanbeaggre-
this,thePMErequiresasampleofchargepricedataand gated directly, but encrypted prices must be estimated.
associated features to train a machine learning model. Therefore,YourAdValueretrievesfromthePriceModel-
This component is designed to incorporate data such as ing Engine a model M(Si) that (i) includes the features
offline weblogs (see Section 5), or online anonymous Si thatneedtobeextractedfromthecollectedmetadata,
contributions (anonymized features and charge prices) and(ii)providesadecisiontreefortheestimationofan
fromparticipatingusers,similarlytootherworksonon- encryptedpricebasedonthesefeatures.
line data and ad transparency (e.g., Floodwatch [77]). Byfollowingthismodel,thebrowserextensionisable
Using such data, the PME can re-train the computed to estimate locally on the client side, the value ESe(S)
i
model at any time. To assess the difference between oftheencryptedchargepricesbasedonthefeaturesS of
i
cleartext and encrypted price distributions in the wild the given nURL. After estimating each encrypted price,
andfine-tunethetrainingmodel,thePMEcanrunsmall YourAdValue presents to the user the calculated sums
5
Metric D A1 A2 5.1 Datasetanalysis
Timeperiod 12months 13days 8days
Impressions 78560 632667 318964 Weblog Ads Analyzer. In order to process our dataset,
RTBpublishers ∼5.6k/month ∼0.2k ∼0.3k we implemented a weblog advertisements analyzer, ca-
IABcategories 18 16 7 pableofdetectingandextractingRTB-relatedadtraffic.
Users 1594 - - First, the analyzer uses a traffic classification mod-
Table3:Summaryofdatasetandad-campaigns. ule to categorize HTTP requests based on an integrated
blacklist of the popular web browser adblocker Discon-
nect [15].2 Using this blacklist, the analyzer catego-
Type Feature
rizesdomainsin5groupsbasedonthecontenttheyde-
Timeofday,Dayofweek
liver: (i)Advertising,(ii)Analytics,(iii)Social,(iv)3rd
Geo-temporal LocationofuserbasedonIP,#ofunique
partycontent,(v)Rest.Itconsequentlyappliesasecond-
locationsoftheuser,Userlocationhistory
level filtering on the advertising traffic by parsing each
Interestcategoriesoftheuser,Typeofmo-
URL for any RTB-related parameters (like nURL). The
bile device, # of total web beacons de-
tectedfortheuser, #ofcookiesyncsde- analyzer detects nURLs by applying pattern matching
tectedoftheuseruptonow,#ofpublish- againstalistofmacroswecollectedafter(i)manualin-
User ersvisitedbytheuser,#oftotalbytescon- spection and past papers [55, 63], and (ii) studying the
sumedbytheuser, existingRTBAPIs[70,27,64,37,58]usedbythecur-
Avg. numberofreqsperuserforthead- rent dominant advertising companies. From these de-
vertiser,#ofHTTPreqsoftheuser,Avg. tectednURLs, itextractsthechargedpricespaidbythe
numberofbytesperreqofuser,Totaldu- winning bidders, after filtering out any bidding prices
ration of reqs of the user, Avg. duration
that may co-exist in each nURL. It also extracts addi-
perreqoftheuser
tional ad-related parameters such as ad impression ID,
Sizeofad,ADXofnURL,DSPofnURL,
bidder’s name, ad campaign ID, auction’s ad-slot size,
IABcategoryofthepublisher,popularity
carrier,etc.
ofparticularad-campaign,
Ad #oftotalHTTPreqsoftheadvertiser,#of Other operations carried out by our analyzer include:
bytesofHTTPreq, Avg. durationofthe (i)userlocalizationbasedonreverseIPgeo-coding,(ii)
reqs for the advertiser, # of URL param- separationofmobilewebbrowserandapplicationorigi-
eters,Numberoftotalbytesdeliveredfor natedtrafficbasedontheuser-agentfieldofeachHTTP
theadvertiser request, (iii) extraction of device-related attributes from
Table4:Featuresextractedbysummarizingdatafromparametersem- the user-agent field (type of device, screen size, OS
beddedineachpricenotificationdetectedinthedatasetforusersand etc.), (iv) identification of cooperating ADXs - DSPs
advertisers.
pairs, leveraging the nURL used by the ADX to inform
the bidder (i.e. DSP) about its auction win, (v) user in-
terestprofilebasedonwebbrowsinghistory.
C (T) and E (T) along with relevant statistics and the
u u
Feature extraction. There are different machine learn-
totalamountV (T)paidbyadvertisers(seeSection 7).
u
ing algorithms DSPs could use for their decision en-
gines. They can take various features as input, each af-
fecting differently the bidding price and, consequently,
thechargedpriceofanad-slot. Toidentifysuchimpor-
5 BootstrappingPME tant parameters, we extracted several features from the
nURLsofourdatasetsuchasusermobilitypatterns,tem-
poral features, user interests, device characteristics, ad-
Inordertoassessthefeasibilityandeffectivenessofour slot sizes, cookie synchronizations [1], publisher rank-
methodology,webootstrapthePMEandtrainourmodel ing,etc. Next,wepresenttheanalysisofthemostinter-
onrealdatabycollectingayearlongdatasetcontaining estingfeatures(Table4presentsasummary). Wegroup
weblogs from 1594 volunteering mobile users from the them into 3 categories: geo-temporal state of the auc-
same country. Our users agreed to use a server of our tion(§5.2),user’scharacteristics(§5.3),andad-related
controlasaproxy,thusallowingustomonitortheirout- (§5.4).
goingHTTPtraffic1. Asaresult,wewereabletocollect
a large dataset D of 373M HTTP requests spanning the
5.2 Geo-temporalfeatures
entireyearof2015.Notethatthoughourdatasetconsists
of HTTP-only traffic, in principle our approach works
An important parameter that could affect a price of an
withHTTPSaswell,usingasinputthecontributeddata
ad-impressioninaRTBauctionistheuser’scurrentlo-
oftheusersascanbeseeninFigure4.Table3presentsa
cation, information which is broadly available to pub-
summaryofourdatasetD.Next,wepresentthedatacol-
lishersandtrackers. Therefore,inourdatasetweextract
lection andanalysis to extract featuresused in theprice
user IP address and using the publicly accessible Max-
modelingandad-campaignplanning.
Mind geoIP database [56], we map each IP to its city
level. In Figure 5, which presents the min, 20th, 50th,
1Nopersonallyidentifiableinformationwasgatheredorusedand 2Ouranalyzercanalsointegratemorethanoneblacklists(e.g.,Ad-
alldatausedweretreatedanonymously. blockPlus’Easylist,Ghostery’sblacklist,etc.)
6
Charged price (CPM) 0 1 0.100 .10101 MadrBidarceVloanleanSceLiavoillDceoast iHVoeinllramsvZaicanioraassgTao odzreae lGOl?rd?a?n?Cand?a?rdoba Charged price (CPM) 01 0.100 .10011 12 - 3 A4M - 7 AM8 - 11 A1M2 - 3 P4M - 7 PM8 - 11 PM Charged price (CPM) 01 0.100 .10011 SundaMyondaTyuesdWayedneTshduarsydFariydaySaturday
Time of day Day of week
Figure 5: Distribution of charged prices for Figure6:Distributionofchargepricesfordif- Figure7:Distributionofchargepricesfordif-
differentcities(sortedbydecreasingcitysize). ferenttimesofday. ferentdaysofweek.
100% 1.8% 100 100%
RTB share 246800000%%%%% 1A2ndr3oid4Mon5th 6of Wt7hien d8yoewa9rs101112 RTB share per OS 0000111.......012468246%%%%%%%%% 1 2 3 4Mon5th 6of t7he 8yea9r 101112 Charged price (CPM) 0 0.10 .1011 Android iOS CDF 1234567890000000000%%%%%%%%%% 0.01 0.1 1 IIIIIAAAAA IIIII1AAAAABBBBB0BBBBB111121235925792 100
iOS Other Android iOS Mobile Device OS Charged price (CPM)
Figure8:PercentageofRTBtraf- Figure9:PercentageofRTBtraf- Figure10:Distributionofcharge Figure11:CDFofthegenerated
ficfortopmobileOSes. ficnormalizedpermobileOS pricesfordifferentmobileOSes. cost by each IAB category for a
2-monthperiod.
95th percentile and max of the charged prices, we see pressions rendered in the different OSes. Surprisingly,
that although the median values are relatively lower in although Android-based devices are more popular, we
largecities,thefluctuationoftheirpricevaluesishigher. see in Figure 10 that iOS-based devices tend to receive
Anotherimportantfeatureistime,andspecificallythe higherRTBprices,inmedianvalues.
timeofdayanddayofweek.Thisisimportantduetothe Inference of the user’s interest. The browsing history
different level of attention a user may give to an ad im- of a user is highly related with her interests. By moni-
pression and the amount of time she has to purchase an toring the websites a user visits through time, a tracker
advertised product (e.g., working hours vs. afternoon’s can infer her interests, political or sexual preferences,
free time, or weekdays vs. weekends). From Figure 6, hobbies, etc., quite accurately [6]. To enrich our set of
although the median charge prices are of similar range, featureswiththeusers’interests,wecollectalltheweb-
the early morning hours until noon tend to have more sites each user visits across her whole network activity.
chargepriceswithincreasedvalues. Similarly,fromFig- Such information is available to the RTB ecosystem as
ure 7, we see that on Sundays the maximum attracted well, by using cookie synchronization [1] or web bea-
chargepricesarehigherthantherestofthedays. cons[36]. Toextracttheinterestsfromthevisitedweb-
sites,weretrievetheassociatedcategoriesofcontentfor
eachwebsiteaccordingtoGoogleAdWords[29]. Then,
5.3 User-relatedfeatures we aggregate across groups of categories for each user
andgetthefinalweightedgroupofinterestsforeachuser
Device type. By parsing the user-agent (UA) header in the form of IAB categories [39]. Figure 11, presents
information, our analyzer classifies traffic and inspects for the top mobile ADX (MoPub) a distribution of the
thedifferentfingerprintstheUAleaks(specificationsof generatedadrevenueforthedifferentIABcontentcate-
processvirtualmachine(e.g., DalvikorART)orkernel gories in a 2 month subset of our dataset. As expected,
(e.g., Darwin), operating system, browser vendor etc.) not all IAB categories cost the same. Indeed, there are
Thus, we are able to identify the type of device (PC or categoriesthatareassociatedwithproductsabletodraw
mobile),thedifferenttypesofmobileoperatingsystems higher bid prices in auctions like IAB-3: Business &
(Android,iOS,Windows)andifthetrafficwasgenerated Marketingwithanaveragechargedpriceofupto5CPM
fromamobileapplicationoramobilewebbrowser. forthe50%ofthecases.Ontheotherhand,therearecat-
In Figure 8, we see the percentage of RTB traffic for egorieslikeIAB-15:Science), whichareunabletodraw
thedifferentOSesovertime. Asexpected,Androidand priceshigherthan0.2CPMforthe50%ofthecases.
iOS dominate, owning the larger portions of the mar-
ket through the entire year, with Android-based devices
appearing in 2x times more RTB auctions. However, 5.4 Ad-relatedfeatures
when normalizing this RTB share per mobile OS (Fig-
ure 9), we find that Android and iOS devices are deliv- Ad-slot sizes. Some ad-entities carry in their nURLs a
ered mostly equal RTB impressions, with some months parameterwiththesizeoftheauctionedad-slot. InFig-
AndroidsurpassingiOSandvice-versa.Then,weextract ure12weplotthepopularityofeachofthead-slotsizes
the traffic originated from the most popular ad-entity, throughtime.It’sinterestingtoseethat300x250ad-slots
MoPub [59], and analyze the charged prices of the im- (knownas“MPUs”or“MediumRectangles”)dominate
7
100% ad-slot sizes: the charge prices but in a rather contradicting fashion:
300x50 though there are more Android devices, iOS-based de-
320x50
80% 468x60 vices draw higher median prices. As expected, the to-
200x200
316x150 tal revenue per category of user interest (through IABs)
728x90
B share60% 213382030006xxxx262250580000 verasr.ieFsinaallolyt,,wthiethdissopmlaeyIrAeaBl-sebsteaitnegomccourpeiceodsbtlyyatnhaand-ostlho-t
RT40% 186000xx610300 doesnotcorrelatewellwithprice. Infact,largerad-slot
400x300 sizes do not mean higher prices. As shown in the next
320x480
20% 480x320 section, these extracted features are used to plan effec-
300x600
350x600 tivead-campaignsandmodelencryptedchargeprices.
0%
1 2 3 4 5 6 7 8 9101112
Month of the year
6 ChargePriceEstimation
Figure12:Ad-slotsizepopularitythroughtime(sortedbyareasize).
In order to create a model that estimates the encrypted
10 100 %
Charged price (CPM) 0 0. 00.00 .1111 Total revenue (CPM) 110 %% ptteoovrtiehacrlae,vscseoudsgcethtreaocdudtvnaeetddartstoiresnutetrhtishsoepnnauoycstehferaoa’rrssgyhebedrtrooewpnaesccrersqryoupanitnraeedld.dcpaoOrtmianc,eepwsu.wteeHasnyoetwhetode-
0.0001 0.1 % obtainthisinformationistocollaboratedirectlywithan
320x50468x60728x90120x603000x251060x603000x600 320x50468x60728x90120x603000x251060x603000x600 ADXthatsendssuchencryptedprices.3 Weassumethis
Ad-slot sizes Ad-slot sizes to be the rare case, since ADXs are unwilling to share
suchkindofdatathatmayrevealbiddingstrategiesand
Figure 13: Distribution of the Figure 14: Accumulated rev-
charged prices per ad-slot size enueofthedifferentad-slotsizes revenues.
(sortedbyareasize). (sortedbyareasize). To address this lack of insight on encrypted prices,
we designed our system able to run small probing ad-
the dataset from May on, taking the place of 320x50
campaigns through such ADX(s) and collect the win-
ad-slots (known as “large mobile banners”). In fact,
ning prices via reports from the collaborating DSP(s).
300x250 ad-slots have more ad content available from
Thesead-campaignscanbedesignedandexecutedwith
advertisers,sotheycanincreaseearningswhenbothtext
the help of a single or few DSPs any time, with little
and image ads are enabled. In addition, we see that the
overhead and a small budget of a few hundred dollars
728x90ad-slot(“Leaderboard”or“banner”)isalsopop-
each. Inaddition,theycanbeoptimizedbyusingaspe-
ular. Thisad-slotisusuallyplacedatthetopofapageto
cific set of experimental setups, which cover all possi-
beseenbytheuserimmediatelywhenthepageloads.
ble scenarios from the small parameter vector S to be
Itiseasytoanticipate, thatthemorespaceanad-slot
keptshort, efficientandcheap. Giventhatthepricesdo
coversintheuser’sdisplay,thehigherthepricewillbe.
not change drastically over time, these campaigns can
To verify this intuition, we isolated the traffic of an ad-
be executed every few months to collect probing data
entity (i.e. Turn [19]), which carries the ad-slot size in
fortime-shiftcorrectionandincreasedcoverageofmore
itsnURLsalongwiththeassociatedchargedprices. Sur-
ADXs. Besides,theycanbeautomatedandre-launched
prisingly,inFigure13,weseethatthisintuitioniswrong
asfrequentlyasneeded,e.g.,everyfewmonthsorwhen
sincethemostexpensivead-slotsforanadvertiserarein
thedetectedcleartextpricesdeviatefromhistoricaldata.
fact,notthelargestones. Inourdataset,weseethatthe
Having such campaigns launched from a specific loca-
twomostexpensivead-slotsaretheMPU(300x250)and
tion allows for more accurate and cost efficient price
Monster MPU (300x600), with median prices of 0.47
modelingthatcanbesharedacrossallparticipatingplat-
and 0.39 CPM respectively. However, from Figure 14,
formusersinthesameareaorcountry.
the increased popularity of MPU and Leaderboard ad-
We envision that these campaigns can be crowd-
slots,allowsthemtoaccumulate64.3%and20.6%ofthe
funded (e.g. like Tor Project [78], Wikipedia,
total RTB revenue of Turn in our dataset, respectively.
WiGLE [9], etc.), thus contributing to an independent
Finally,itisworthtonotethatourresultsverifypastre-
and sustainable platform that can scale better across
sources[16,26]regardingthemoreexpensivead-slots.
users,countries,andADXscovered. Onemayarguethat
these probing campaigns could pollute users’ browsing
5.5 Summary
with non-useful ad impressions. Therefore, they need
to comply with the current standards, and if possible,
In summary, by analyzing the features extracted from
consideranactualproductorservice. Ofcourse,ADXs
ourofflinedataset,wefindthatauser’slocation(atcity
couldinprinciplefightbackandtrytoidentifyandblock
level)affectsthemedianpricethatadvertiserswouldbe
suchcampaigns, buttheirhugeclientelecombinedwith
charged,butalsoitsvariability. However,suchpricedif-
the low volume of such campaigns makes the detection
ferencesareexpectedtobemoreevidentatthecountry-
very difficult. In the next paragraphs, we first describe
level, as shown in [63]. In addition, the days and hours
thatausermaynotbebusy(Sundays),ormayoffermore
3WeconsideredADXsforencryptedpricessuchasDoubleClick,
attention(e.g.,earlymornings,Mondays)leadtohigher
OpenX, RubiconProject and PulsePoint, as well as ADXs that send
charged prices. The type of user’s device also affects cleartextpricessuchasMoPub(thetopmobileADX).
8
the effort to select a subset of core features important Filtername Rangeofvalues(type)
forpricemodeling(§6.1)andhowtheyallowustode-
Cities city1,city2,city3,city4
sign efficient and effective ad-campaigns (§ 6.2). Then,
Typeofonlineinteractions Mobilein-app,Mobileweb
weprovideananalysisofthedatacollectedbytwosuch
Timeofday 12am-9am, 9am-6pm, 6pm-
campaigns(§6.3)and,finally,wedescribethemodelthat 12am
estimatesencryptedpricesandcanbeusedbyend-users Dayofweek Weekday,Weekend
(§6.4). Typeofdevice Smartphone,Tablet
Typeofoperatingsystem iOS,Android
Ad-formatforsmartphone 320x50,300x250,320x480or
6.1 Dimensionalityreductionoffeatures 480x320
Ad-formatfortablet 728x90, 300x250, 768x1024
Inordertoperformad-campaignsthatarebotheffective or1024x768
andcostefficient, weneedtoselectasubsetoffeatures Ad-exchange MoPub, OpenX, Rubicon,
S⊆F from the available ones F that best describe the DoubleClick,PulsePoint
RTBpricesfoundinweblogssuchasthehistoricdataset Categoriesoftargeting allIABspossible
D. Thissubsetoffeaturesshouldexplainasmuchofthe Table 5: Basic filters used in controlled ad-campaigns in a specific
variabilityofpricesaspossible,butshouldalsobesmall. country.Intotal,144experimentalsetupswereattempted.
The fewer features we select as important, the smaller
thecostofrunningprobingad-campaignstocollectrep-
the receiver operating characteristic curve (AUCROC)
resentativeRTBpricesusingthesefeatures.
and out-of-bag error. Dimensionality reduction could,
Toachievethisselection,weperformeddimensional-
in principle, lead to loss of accuracy in the effort to ex-
ity reduction using all the available features (288) de-
plain price classes. However, our experimentation lead
scribedinSection5,usingthecleartextpricesasthetar-
to a small subset of features with minimal loss of pre-
get variable for optimization. First, for normalization,
cision (<2%) and recall (<6%). In fact, we conclude
we applied a log transformation on the extracted cleart-
that an optimal subset that performs very well and is
ext prices found in D. Then, we applied a clustering of
small enough to allow cost efficient ad-campaigns is a
thepricesinto4classes,usinganunsupervisedequidis-
setthatcombinesfeaturesfromdifferentgroups. Inpar-
tance model that finds the optimal splits between given
ticular,(alsoconfirmedwithanad-campaignexpert)we
prices using a method of leave-one-out estimate of the
select the following features to be used for the prob-
entropyofvaluesineachclass. Next,wefilteredoutfea-
ing ad-campaigns described next: S={application/web-
turesthatdidnotvaryatall(i.e.,constants)orhadvery
browsing,devicetype,userlocation,timeofday,dayof
highvariance(99%)(i.e.,likelytobenoise).
week,adformat(size),typeofwebsite,ad-exchange}.
As a final step, dimensionality reduction (or feature
selection)techniquessuchasPCAorrandomforestscan
beused[46].WechosetheRandomForestsmodel(RF)4
6.2 Ad-campaignssetup
because it takes into account the target variable (cleart-
ext price), it can be trained quickly on large datasets, it
maintainsinterpretabilityoffeaturesandgenerallydoes Using the most important parameters extracted in set S,
not overfit the given data. In case the availability of weconstructvariousexperimentalsetupss∈S⊆F that
cleartext prices is limited, the reduction step to identify can be used to deploy such ad-campaigns over a short
important features to be used in ad-campaigns could be periodoftimeT(cid:48) inselectedADXs. Thesesetupscom-
hindered. To mitigate this, the PME can use interme- binedifferentvaluesofcontrolvariablesthatareimpor-
diate techniques such as high correlation filters that do tantforanad-campaign:<userlocation,web-interaction
notrequireatargetvariable,toeliminatefeaturescarry- type, time of day, day of week, device type, OS, ad-
ing similar information. We trained various RF models size,ADX>. Forexample,anexperimentalsetupcould
using subsets of semantically related features from the be this: <New York, app, 12am-9am, weekday, smart-
availablefeaturesetandthebestfeaturesfromeachsub- phone,iOS,320x50,MoPub>(144setups,Table5). By
set were selected based on their power to describe the running such controlled ad-campaigns, we can receive
cleartextpricedistribution.Insummary,wegroupedfea- groundtruthdataaboutencryptedprices,therebyallow-
tures in the following sets: A) time, B) http-related, C) ingustotrainamodelforsuchprices. Campaignswith
advertisement-related,D)DSP-related,E)publisher/host ADXsthatdelivercleartextpricesalsoallowustocom-
interests,F)userhttpstatistics(historical),G)userinter- pare prices in different times and compute shifts in the
ests (historical), and H) user locations (historical). We price distribution due to time passed between the col-
alsotriedselectingrepresentativefeaturesoutofeachset lection of dataset D and present time. To compensate
to create minimal combinations covering all aspects of forthelossofinformationfromcleartextpricesbecom-
thehttp-availableinformation. ing less abundant, additional features available in pro-
Intotal,wetriedtensoffeaturesubsetsandcombina- fessional ad-campaign planners (such as in FDVT [14])
tions and evaluated them using standard machine learn- couldbeusedinthefuturetoenhancethesetupstested.
ingmetricssuchasprecision,recall,weightedareaunder Withtheresultsofthesecampaigns(inessence,charged
prices for RTB ads that fulfil a given setup s), the PME
4Anensembleofdecisiontreesbuiltusingarandomsubsetfrom cantrainamodeltoestimatethecostofnewadswitha
theavailablefeatures. givensetups(cid:48)close,orequal,toonetested,i.e.s(cid:48)∼s∈S.
9
100
Number of required ad-campaigns. Two important dataset (2m - Mopub)
questions while designing these probing ad-campaigns PM) 10 ada-dc-acmampapiaging n(e (nclcerayprtteexdt))
are how many ad-campaigns and impressions per cam- e (C
paign are enough to acquire a good approximation of c
pri 1
the mean of price distribution per campaign. For this, d
e
we analyzed the ad-campaigns found for MoPub in D. g
ar 0.1
We identified 280 such campaigns in 2015, with mean h
C
and standard deviation of charge price of m=1.84 and
0.01
std =2.15 CPM, respectively. We use the process de- IAB1 IAB12 IAB13 IAB17 IAB19 IAB20
scribed in [41] and the next formulation to compute d, IAB categories
the expected error on the mean, assuming a suggested
Figure15:ComparisonofCPMcostsforthedifferentIABcategories
number of setups n, and ignoring the finite population inourdatasetandthe2probingad-campaigns.
correction adjustment (thus assuming a more conserva-
some cases, the results from the dataset D vary more
tiveapproximationofn):
thaninthead-campaigns. Thisistobeexpected,asthe
Z ×std dataset includes prices from numerous DSP-ADX pairs
α/2
d= √ , (4) for many ad-campaigns running in parallel in the dura-
n
tion of a year, whereas our two ad-campaigns are more
targetedtospecificDSP-ADXpairs.
whereZisthez-scoreofnormaldistribution. Usingthe
Regarding the cleartext prices of different IAB cat-
144 setups proposed, we can approximate to more than
egories, although the median prices are usually in the
95%CI(i.e. α =0.05)themeanpriceofcampaignsob-
same order of magnitude, they are higher in the case of
servedinthewild,assumingamarginoferror0.35CPM.
therecentad-campaigncontrarytothe2monthdataset.
Also, considering the distribution of prices within the
We believe that this difference is due to the time shift
largest of ad-campaigns detected for MoPub with 1.8k
between the dataset collected in 2015 and the ad cam-
impressions, we can approximate to 95% CI the mean
paigns performed in 2016. In addition, we see that the
priceofacampaign,assumingamarginoferror0.1CPM
medianpriceisalwayshigherincaseofencryptedprices
andminimumof185impressionspercampaign.
(A1), comparedtothecleartextpricesofthesecondad-
campaign(A2),andthedatasetD.
6.3 Ad-campaignsanalysis
6.4 Encryptedpricemodeling
Usingtheseasguidelines,weexecutedtworoundsofdif-
ferent ad-campaigns to collect data on prices (Table 3).
Usingthegroundtruthdatacollectedfromthefirstround
Noteatthispoint,thatourad-campaignsadvertisedareal
ofad-campaignswithvariousparameterswithinthesub-
service in order to avoid polluting the users with mean-
setoffeaturesS,wetrainedamachinelearningclassifier
inglessimpressions.
topredictvaluesofencryptedprices. Asafirststep,we
Datasetcollected. Thefirstround(A1)wasexecutedfor
performedsimilarpreprocessingasearlierfortheclear-
2weeksinMay2016andutilizedthe4ADXsmentioned
text prices (normalization and clustering to 4 classes of
earlierthatencryptpricenotificationsandtargetedpub-
well balanced groups). Next, we trained a RF model to
lishersofmanyIABcategories. Thesecondround(A2)
predict the class value of an encrypted price, based on
was executed with the same experimental setups as A1
the available parameters S. Using features such as city
duringJune2016,butinthiscasetheDSPwasinstructed
of user, day of week and the time the ad was delivered,
touseonlyMoPub,whilestilltargetingsimilarIABcat-
ad size, mobile OS of the user’s device, IAB category
egories of publishers. In both campaigns, the DSP was
of the publisher, ADX used and device type, our clas-
givenanupperboundonthebiddingCPMprice,tosafe-
sifier can achieve 82.3% accuracy and 0.96 AUCROC.
guard that the allocated budget will not be consumed
When the publisher used is also taken into account in
veryquickly. Ontheotherhand,theDSPwasinstructed
themodel,theperformanceoftheclassifierincreasesto
tobidinadynamicmanner,asloworhighasneededto
95%,and0.99AUCROC.However,thisisclassicover-
gettheminimumofimpressionsdeliveredforthevarious
fittingandweshouldcautionthatthepublishersusedin
experimentalsetupswerequested. Overall,wemanaged
the ad-campaigns are just a subset of the thousands of
toreceiveforallsetups,over600kimpressionsdisplayed
possible publishers that can be found in real weblogs.
withencryptedpricenotificationstomorethan200pub-
Therefore, we chose to use the model without the pub-
lishers, and over 300k impressions with cleartext price
lisheraspartofitsinputfeatures. Next,thismodelwas
notificationstomorethan300publishers,reachingaudi-
usedfortheestimationoftheencryptedpricesofnURLs
ences of 6 IAB categories common to both price notifi-
foundintheweblogsofeachuserinD,giventhematch-
cationtypes.
ingparametervaluesfromS⊆F.
Cost paid vs. IAB category. In Figure 15, we com-
paretheoverlappingIABcategoriesoftheRTBimpres-
sions we took from (i) the set of encrypted prices from 7 UserCostforAdvertisers
the ad-campaign on four ADXs in A1, (ii) the set of
cleartext prices from the ad-campaign on MoPub (A2), The previous sections allowed us to: (1) bootstrap our
a (iii) the 2 months Mopub subset of D. Note that in price modeling engine from existing user weblogs, so
10