Is More Always Merrier? A Deep Dive Into Online Social Footprints

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1 Is More Always Merrer? A Deep Dve Into Onlne Socal Footprnts Terence Chen, Mohamed Al Kaafar, Ark Fredman, and Roksana Borel Natonal ICT Australa Unversty of New South Wales, Australa INRIA, France frstname.lastname@ncta.com.au ABSTRACT We present an emprcal study of personal nformaton revealed n publc profles of people who use multple Onlne Socal Networks (OSNs). Ths study ams to examne how users reveal ther personal nformaton across multple OSNs. We frst consder the number of publcly avalable attrbutes n publc profles, based on varous demographcs and show a correlaton between the amount of nformaton revealed n OSN profles and specfc occupatons and the use of pseudonyms. Then, we measure the complementarty of nformaton across OSNs and contrast t wth our observatons about users who share a larger amount of nformaton. We also measure the consstency of nformaton revelaton patterns across OSNs, fndng that users have preferred patterns when revealng nformaton across OSNs. To evaluate the qualty of aggregated profles we ntroduce a consstency measure for attrbute values, and show that aggregaton also mproves nformaton granularty. Fnally, takng Australan phone drectory as a case study, we demonstrate how the avalablty of multple OSN profles can be exploted to mprove the success of obtanng users detaled contact nformaton, by cross-lnkng wth publcly avalable data sources such as onlne phone drectores. Categores and Subject Descrptors H.4.m [Informaton Systems]: Mscellaneous General Terms Measurement Keywords onlne socal network, socal footprnt, prvacy 1. INTRODUCTION Sources of nformaton about ndvduals, ncreasngly collected n numerous locatons, nclude many aspects of people s lves. Databases wth records of health, educaton, employment, fnance and other personal nformaton are beng created and updated by varous government departments and busnesses that ndvduals deal Permsson to make dgtal or hard copes of all or part of ths work for personal or classroom use s granted wthout fee provded that copes are not made or dstrbuted for proft or commercal advantage and that copes bear ths notce and the full ctaton on the frst page. To copy otherwse, to republsh, to post on servers or to redstrbute to lsts, requres pror specfc permsson and/or a fee. WOSN 12, August 17, 212, Helsnk, Fnland. Copyrght 212 ACM /12/8...$1.. wth. Search engne provders and onlne retalers collect data about end-user nterests, spendng habts and other onlne actvtes. Fnally, the ndvduals also contrbute to the nformaton avalable about them by makng t accessble on ther homepages and on varous onlne personal profles, and by partcpatng and expressng ther vews n selected onlne fora. Onlne Socal Networks 1 (OSNs) are a rch source of nformaton about ndvduals. It may be dffcult to justfy the clam that the exstence of publc profles breaches the prvacy of ther owners, as they are the ones who entered the data and made them publcly avalable n the frst place. However, aggregaton of multple OSN publc profles s debatably a source of prvacy loss, as profle owners may have expected each profle s nformaton to stay wthn the boundares of the OSN servce n whch t was created. In ths paper we descrbe an emprcal study based on 18,1 onlne profles of 34,9 users across ten major OSNs. Based on ths study, we make the followng novel contrbutons: Cross-OSN profle analyss: we evaluate the sze of the onlne socal footprnt [], represented by the number of publcly avalable attrbutes n lnked OSN profles, and characterze t based on varous demographcs, ncludng occupaton and the use of pseudonyms. In addton, we ntroduce a measure of complementarty between OSNs to evaluate how much nformaton s ganed through aggregaton of profles. We fnd that on average, more than half of the combned set of attrbutes from any two profles of the same user are complementary to each other. However, there s also a correlaton between the average number of attrbutes revealed n a sngle OSN and the number of OSN profles owned by the user, ndcatng that a larger footprnt sze may also result from users nclnaton to share more nformaton. Consstency and data qualty: we measure the consstency of attrbute revelaton patterns across OSNs, and compare t wth respect to a random model n whch revelaton patterns on dfferent OSNs are ndependent. Our observatons ndcate that users have preferred patterns when revealng nformaton across OSNs. We also evaluate the qualty of aggregated profles by measurng the consstency of attrbute values. We fnd that over 8% of the users have more than % matchng attrbute values across dfferent OSNs. Moreover, profle aggregaton also enables an ncrease n the granularty of the collected nformaton, further mprovng ts qualty. The hgh level of consstency of attrbute revelaton pattern and attrbute values suggests that users are vulnerable to lnkng attacks whch combne ther onlne profles and other datasets. Beyond onlne socal footprnts: takng the Australan phone drectory as a case study, we demonstrate that profle aggregaton 1 In ths paper we use the term OSN broadly and apply t to any onlne servce n whch users share nformaton wth frends, work colleagues or others. 7

2 goes beyond larger onlne socal footprnts, makng t easer to gather user nformaton from other publc data sources. Our results show that addtonal nformaton obtaned from aggregated profles mproves the success of record lnkage, ncreasng up to fve tmes the number of unquely dentfed users (and ther correspondng phone numbers and resdental addresses), and up to three tmes the number of users who have fve or fewer matchng records n the phone drectory. We note that our analyss consders only publcly revealed nformaton and as such we do not clam that our fndngs apply to users who tend to hde most of ther personal data. Although the profles we examned are based toward users (we used more than 3K profles as the startng pont for the data collecton), the technques and metrcs dscussed n the paper are general, and provde nsghts nto user behavour over multple onlne servces. The paper s organzed as follows. In Secton 2, we descrbe the data collecton process. We study demographc correlatons n Secton 3 and analyze the complementarty and consstency of OSN profles n Secton 4. Cross-lnkng wth the Australan phone drectory s presented n Secton. We dscuss related work n Secton and conclude n Secton DATA COLLECTION We selected ten target OSNs based on ther popularty and ntended use, amng to represent major OSNs for both socal and professonal. We crawled user profles by vstng ther profle pages and by APIs that are provded by the target OSNs, between May and August 211. As a startng pont, we randomly selected around 3K profles out of the 3 mllon Profles 2 collected by Perto el. al. [12]. We lmted the number of target profles because of the complexty and tme nvolved n the crawlng process. Profles allow users to lnk ther profle to accounts on other OSNs and web servces. As a frst step, we leveraged ths feature to collect lnked accounts on other OSNs. In a second step, we crawled addtonal nformaton sources, such as external web pages and socal aggregaton servces, to assocate users accounts wth profles on addtonal OSNs. Overall, we successfully crawled and parsed a total of 179,188 profles across the ten consdered OSNs assocated to our ntal set of 3K users. Table 1 lsts the target OSNs and the respectve number of collected profles. Interested readers may refer to the techncal report [2] for further nformaton about crawler desgn. OSN Sample sze OSN Sample sze 34,9 1,37 2,499 Flckr 13,713 Last.fm 7,1 LnkedIn 1,2 LveJournal 2, Myspace 9,782 Twtter 27,72 YouTube 2,2 Table 1: Number of collected profles n each OSN 3. ONLINE SOCIAL FOOTPRINTS In ths secton, we frst examne the average number of publcly avalable attrbutes n each of the OSNs ndvdually and when aggregated across multple OSNs. We then consder dfferent demographcs and evaluate the relaton between the use of pseudonyms and the footprnt sze. 2 We unfed attrbute namng varatons across the target OSNs and dentfed 4 types of avalable attrbutes representng dfferent personal nformaton n publc profles (see Table 2 for the full lst). Fgure 1 shows the total number of attrbutes avalable n each OSN and the average number of publcly avalable attrbutes n the collected profles. Whle the number of avalable attrbutes vares sgnfcantly between dfferent OSNs, rangng from to 27 attrbutes, n practce the average number of publcly avalable attrbutes for each user spans a smaller range, between.4 to 1.2 attrbutes. No. of attrbutes AVG No. attrbutes Myspace YouTube LnkedIn LveJournal Flckr Last.fm Twtter Total avalable attrbutes Publcly avalable attrbutes OSNs Number of OSNs 7 2 % of present attrbutes AVG No. of attrs on No. of OSNs (c) Fgure 1: Attrbute avalablty n each OSN. Average number of attrbutes v.s. number of OSNs. (c) footprnt sze v.s. number of OSN profles owned by users. about me/bo emal lnks poltcal age ethncty lved quote annversary famly locaton relgon brth date gender moves schools body type home town musc smoke/drnk books ncome name sports chldren ndustry occupaton status companes nterests organzaton tv shows connectons nterested n orentaton user name educaton language phone zodac sgn Table 2: Attrbute lst To quantfy the amount of nformaton revealed across multple OSNs, we use the concept of onlne socal footprnt proposed by Iran et. al. [], whch represents the collecton of all peces of nformaton that a user exposes on onlne socal stes. For each set of users who have profles on n socal networks, we measured the average number of (non-overlappng) publcly avalable attrbutes. The results are shown n Fgure 1. We found that the average onlne socal footprnt sze steadly ncreases wth the number of OSN profles, n lne wth the results reported by Iran et. al. []. To better understand ths trend, we further explored two contrbutng factors. Frst, as dfferent OSNs provde dfferent types of attrbutes, users wth more profles have a larger selecton of attrbutes from whch to share. Moreover, even for overlappng attrbutes (e.g., the locaton attrbute s avalable on many OSNs), a user may make an attrbute publc on one OSN, whle t s prvate or not populated 8

3 on another. We take a closer look at these aspects n Secton 4. Second, possessng a large number of onlne profles may ndcate a tendency to share more nformaton onlne, not only across several OSNs, but also n each ndvdual OSN. For example, Fgure 1(c) shows the average number of publcly avalable attrbutes on, for each set of users wth a certan number of OSN profles, ndcatng that the two are correlated. A smlar pattern was observed n other OSNs. 3.1 Demographc Analyss We further analyzed the data set to characterze users by varous demographc groups: gender, age, country of resdence and occupaton. We note that out of the total 34,99 users n our data set, 7.94% revealed ther gender n one or more OSN profles; 8.24% dsclosed ther age; 9.9% dsclosed ther occupaton and all users dsclosed ther country of resdence. Wthn the subset of users who revealed ther gender, we dd not detect statstcally sgnfcant relaton between gender and footprnt sze. Smlarly, no such relaton was evdent for country. Fgure 2 shows the average onlne socal footprnt sze by age group. In general, the average footprnt sze does not appear to be sgnfcantly dfferent between age groups, wth a slght ncrease for older age groups. However, we observed that users n dfferent age groups tend to share dfferent attrbutes, for example older users are more lkely to provde relgon and poltcal vew on whle younger users are more nterested n sharng ther favourte musc and books. A smaller footprnt sze was observed for users who dd not share ther age publcly. Fgure 2 presents ten of the most frequent occupatons declared by users, and the average onlne socal footprnt sze for each occupaton. We found that users wth customer-facng occupatons, e.g., real estate or marketng, tend to dsclose a sgnfcantly hgher number of attrbutes than users wth other occupatons, e.g., teachers or students. 22 AVG No. of attrs 21 2 AVG No. of OSNs AVG No. of attrbutes n/a Age group.. 4. AVG No. of OSNs AVG No. of attrbutes Photographer Wrter/Edtor Actor/Actress Real Estate Broker Marketng Consultant Occupaton Teacher Student Scentst Engneer IT professonal Fgure 2: Average footprnt sze by age group and occupaton. 3.2 Pseudonyms and Footprnt Sze Although use of real names n OSNs has become a common practce (ether by nclnaton or to comply wth OSN polcy), a sgnfcant number of users use pseudonyms. In ths secton, we characterze the amount of revealed nformaton based on the use of pseudonyms. For ths analyss we consder only Englsh names, and exclude names n other languages. We dstngushed real names from pseudonyms by comparng names n profles to those observed on LnkedIn, under the assumpton that users provde genune nformaton n ther professonal profle. 3 We dvded profle names nto three categores: full real names, partal names (e.g., frst or last name or some combnaton of them) and unrelated strngs, consderng the last as an 3 Ths assumpton s n lne wth the statstcs reported by Iran et. al. [], Fg. 3, for the use of real names n OSN accounts. ndcator of a pseudonym. We used strng matchng and regular expresson technques to dstngush between these categores. To reduce the nfluence of spellng varatons, e.g., name alases, we used flexble fuzz matchng wth Jaro-Wnkler dstance metrc [7]. Fgure 3 shows the dstrbuton of user name categores n each OSN. Users on, and Flckr mostly dentfy themselves by ther real full or partal names, whle pseudonyms are more prevalent on YouTube, and LveJournal (24%, 29.4% and 41.2% respectvely). LveJournal Youtube Myspace Twtter Lastfm Flckr lnks books moves musc schools nterests companes occupaton aboutme hometown % of users % of avalablty pseudonym full name partal names OSN Attrbute real name users pseudonym users Fgure 3: Dstrbuton of name categores n OSNs; Onlne socal footprnt sze on YouTube per name category. We compared the average onlne socal footprnt sze between users who use ther real names (full or partal) and those who use pseudonyms, and found that on average the latter have a smaller footprnt. As an example, Fgure 3 demonsrates ths for YouTube users. Users who dentfy themselves by ther real names make more personal nformaton publcly avalable: n addton to mandatory nformaton lke name, locaton and age, they share on average 2.2 more attrbutes, whle pseudonym users only reveal.9 addtonal attrbutes. 4. CROSS-OSN ANALYSIS The prevous secton dentfed the ncreased avalablty of nformaton that can be obtaned by combnng multple OSN profles. We now nvestgate n detal the level of nformaton complementarty across OSNs. Then, we queston the exstence of patterns n publcly revealed nformaton across dfferent OSNs and evaluate the consstency of the nformaton users share through ther multple profles. 4.1 Complementarty of Informaton Consder a user U who has two profles p x and p y on OSNs x and y respectvely. In each of these profles, say p x, we represent attrbutes avalablty as a bnary vector v x = [a 1,x, a 2,x,..., a t,x], where a k,x denotes the avalablty of the k th attrbute for user n OSN x. We set a k,x to f the attrbute s not avalable (ether not populated by the user, prvate or altogether not avalable to be flled n that partcular OSN) and a k,x s set to 1 f the attrbute s populated and publcly avalable. We stress that n ths ntal analyss, we do not (yet) consder the consstency of the values across dfferent OSNs profles of the same user, so bnary avalablty vectors are suffcent. We are nterested n the complementarty of two profles,.e., the porton of attrbutes that are avalable n one profle 9

4 but not n another. To ths end, we measure the Jaccard dstance (or Jaccard dssmlarty) between profles, gven by: (v D(vx, vy) x vy) = (v x vy), (1) where (vx v y) s the number of msmatches (XOR) and (vx vy) s the number of non-zero nstances n both vx and vy (OR). The average complementarty score of user U possessng n dfferent OSNs profles s: n 1 n ˆD x=1 y=x+1 = D(v x, vy). (2) n(n 1)/2 % of users (PDF) AVG Jaccard Dssmlarty % of users (CCDF) AVG Jaccard Dssmlarty Fgure 4: Informaton complementarty dstrbuton: Jaccard dssmlarty PDF and CCDF. Fgure 4 shows the PDF and CCDF of the average complementarty scores. We observe n Fgure 4 that almost 9% of the users have an average complementarty score over., whch ndcates that on average more than half of the set of combned avalable attrbutes from any two OSN profles of the same user are complementary to each other. 4.2 Consstency of Informaton Revelaton Patterns Across OSNs To evaluate whether users actually have what could be consdered a personal prvacy polcy,.e., they reveal or hde attrbutes across dfferent OSN profles n a consstent manner, we compare the respectve nformaton revelaton patterns. To ths end, we agan consder the attrbute avalablty as a bnary vector. For the purpose of ths analyss, when comparng two profles p x and p y, we consder only the subset of attrbutes that are avalable n both OSN x and OSN y (regardless of whether these attrbutes are populated for any partcular user or not). Whle our measure s senstve to varatons ntroduced by OSN desgn updates, or the ntroducton of new default settngs, we stll beleve t s ft for purpose, as t provdes a snapshot of users onlne socal footprnt characterstcs at the tme the data was collected. To capture the consstency of nformaton revelaton patterns we consder both postve and negatve matches and we use the Sokal & Mchener smlarty metrc [3]. The metrc takes two bnary vectors and returns a normalzed smlarty score between (no match) and 1 (full match). The smlarty score for vectors v x and v y s computed as: S(v x, v y) = (v x v y) (vx v y), (3) where (v x v y) s the number of bnary nstances that are both equal 1 or (XNOR), and (v x v y) s the number of attrbutes avalable to be flled n both OSN x and OSN y. The average consstency of nformaton revelaton patterns for a user wth n OSN profles s then computed as: Ŝ = n 1 n x=1 y=x+1 S(v x, vy). (4) n(n 1)/2 Some attrbutes may be publcly avalable by desgn or very common (e.g., name and locaton are hghly represented n most OSNs), leadng to hgher smlarty scores between OSNs that share those attrbutes. Smlarly, very rarely revealed attrbutes can also lead to hgh smlarty scores. Therefore, as a baselne for comparson we also plot the expected consstency dstrbuton for a model where attrbutes are revealed randomly and ndependently n each OSN. We generate random patterns usng a based con functon, based on the publc avalablty of each attrbute as observed n each OSN. For nstance 9.% of of users n revealed the attrbute "brthday", then we set the bt n the baselne pattern vector to "1" wth a probablty of 9.%. We then compute the smlarty score wth other OSN for the obtaned random bnary vectors. Fgures and respectvely show the PDF and the CCDF of consstency scores for all users. The observed dstrbuton has a mean of.7, and more than 97% of users have a consstency score hgher than.. Compared to the random model, the observed dstrbuton shows sgnfcantly more consstent patterns, whch suggests that users do have preferred patterns when revealng nformaton across dfferent OSNs. % of users (PDF) Random Observed AVG S&M smlarty score % of users (CCDF) Random Observed AVG S&M smlarty score Fgure : Informaton revealng pattern consstency across multple OSNs: Sokal & Mchener smlarty PDF and CCDF 4.3 Consstency of Attrbute Values In the followng, we measure the consstency of the attrbutes values as populated by users across multple OSNs. The average consstency of attrbute values for user U (wth n OSNs) s computed as: Ĉ = n 1 n {p x } {p } y x=1 y=x+1 (v x vy ) n(n 1)/2, () where {p x} and {p y} are the sets of publcly avalable attrbute values n user U s profles p x and p y respectvely; (vx vy) s the number of attrbutes that are publcly avalable n both profles, and {p x} {p y} s the number of attrbutes wth matchng values. Because attrbute values may have dfferent formats on dfferent OSNs, we apply dfferent matchng decson processes for dfferent types of attrbutes rather than relyng on plan strng matchng. For nstance, we standardze the format of brth date, age, URLs and locaton before comparng the attrbute values. The average value consstency dstrbuton s shown n Fgure, wth a mean of.9% matchng attrbutes. The CCDF n Fgure shows that over 8% of the users have more than % matchng attrbute values across dfferent OSNs. 7

5 % of users (PDF) AVG % of matchng % of users (CCDF) AVG % of matchng Fgure : Attrbute value consstency across multple OSNs: PDF and CCDF Consstency of Locaton Informaton We used Geocodng API 4 to format the avalable locaton nformaton to the precson levels of cty, state and country. The avalablty of locaton nformaton across 9 OSNs (locaton nformaton s not shown n MySpace profles) s shown n Fgure 7. Note that by desgn, the locaton n Last.fm and YouTube profles s restrcted to country level. We observed that for over 88.2% of users, and over % of users on all other OSNs, cty-level locaton was publcly avalable. Snce most OSNs do not restrct the format of locaton n the profles, a number of users flled n nformal locaton names whch may not be recognzed by Geocodng API. Such porton s hghest n Twtter, approxmate 8% and follow by and, about %. % of users % of matches Twtter Youtube Lastfm LnkedIn Youtube Lastfm Flckr OSN Lvejournal OSN Lvejournal LnkedIn Flckr Twtter cty state country not recognzed Match by cty Match by state Match by country Fgure 7: Locaton nformaton: Levels of granularty n dfferent OSNs; Consstency between OSNs. Due to the moblty of users, locaton nformaton may be nconsstent f ther profles across multple OSNs are not up-to-date. To measure such nconsstency, we compare the locaton nformaton n each OSN profle to other profles wth the granulartes of cty, state and country. Fgure 7 shows the percentage of locaton nformaton matches when the nformaton retreved from one partcular OSN s used as a reference (consderng the smallest avalable locaton granularty n both OSNs). As llustrated n Fgure 7, a vast majorty of users provde consstent country and state-level nformaton (on average 93% and 8% respectvely) across the OSNs. The proporton of cty-level matches s surprsngly hgh, ndcatng that at least half of the users are wllng to share the cty where they lve across multple OSNs. For example, when consderng 4 LnkedIn as a reference, the cty-level locaton provded by users on the other OSNs matches n 1% of the cases.. BEYOND ONLINE SOCIAL FOOTPRINTS THE AUSTRALIAN PHONEBOOK CASE In Secton 4 we showed how aggregaton of nformaton across OSNs allows gatherng addtonal nformaton through complmentarty, and evaluatng ts qualty through consstency. However, aggregaton contrbutes also to mproved nformaton granularty. Fgure 8 shows the mproved avalablty of locaton nformaton n dfferent granularty levels on average as more OSN profles are aggregated. % of users cty state country Number of OSNs Fgure 8: Increasng locaton granularty by aggregaton We stpulate that the aggregaton of nformaton over multple OSNs may go beyond the ncreased onlne socal footprnt sze and the hgher granularty of nformaton. It also makes t easer to gather user nformaton from other publc data sources, thereby contrbutng to prvacy loss and ncreasng the rsk of dentty theft. To demonstrate ths, we use an electronc copy of the Australan phone drectory, whch ncludes the regstered users surname and ntal, full address (street number, street name, suburb and state) and phone number. In partcular, we show how granular locaton nformaton from aggregated OSN profles helps uncover even fner address detals as well as phone numbers of the owners of these profles, although they may have preferred these detals not to be assocated wth ther profles (e.g., phone numbers were publcly avalable for only 2% of users n our dataset. In contrast, Dey et. al. [4] observed that hometown and cty detals are ncreasngly shared publcly). As a frst step, we calbrated the search radus used to match locatons reported n OSNs to phone drectory lstngs. To ths end, we used a control data set obtaned from 42 volunteers around the greater regons of Sydney, Melbourne and Brsbane, of whom 2 (9.%) were lsted n the phone drectory, and 17 (4.%) were not. The volunteers provded ther full names, OSN dentfers for two or more OSNs from the lst n Table 1, and ther resdental addresses. To measure accuracy we consdered the true postve rate (TPR),.e., the rate of lsted users whose real records were found wthn the gven radus, and the false postve rate (FPR),.e., the rate of unlsted users for whom false records were retreved. Usng the locatons extracted from users OSN profles, we progressvely ncreased the radus around the provded locatons and measured the resultng TPR and FPR. The dstance between two locatons, knowng ther lattude and longtude, was computed based on Haversne formula [1]. Table 3 shows sample values of the search radus and the correspondng search accuracy. We found 2km to provde the best balance between TPR and FPR. We observed that most Australan OSN users ndcate a greater cty area as ther locaton (for exam- The Australan Resdental Database, 71

6 ple, n the phone drectory, Sydney refers only to the central busness dstrct rather than the greater Sydney area), and the chosen radus s consstent wth ths observaton. No. of users from the dataset 12 1 Range TPR FPR Range TPR FPR cty 33.3% 27.8% 3 km 9.8% 77.8% 2 km 41.7% 38.9% km 9.8% 83.3% km 4.8% % 1 km 9.8% 83.3% 1 km % % state 1% 88.9% 2 km 91.7%.% country 1% 94.4% Table 3: Search radus v.s. TPR and FPR values. no background nfo wth Profles wth multple OSNs nfo No. of matchng records Fgure 9: Combnng OSN profles and phone drectory to gather contact detals. In the second step, we used a subset of the dataset descrbed n Secton 2, takng only Australan users whose surname was lsted n the phone drectory, resultng n a data set wth user profles. For each profle, we measured the number of matchng records n the drectory for three cases: matchng usng only the surname; ncludng also locaton from a sngle OSN ( Profles); and ncludng granular locaton nformaton aggregated over multple OSNs. Fgure 9 shows the dstrbuton of the number of matchng records for each of these cases. The results ndcate that the added nformaton makes record lnkage much easer, ncreasng up to tmes the number of users who are unquely dentfed, and up to 3 tmes the number of users who have or fewer matchng records.. RELATED WORK Pror work on evaluatng the nformaton revealed by aggregaton of publc profles from multple OSNs focused on measurng the quantty of attrbutes present n these profles. Krshnamurthy et. al. presented results for both moble [8] and fxed [9] OSNs. Iran et. al. [] presented the onlne socal footprnt, consstng of the aggregate nformaton of OSN account owners. In a later work [] they expanded the concept further and analyzed the threat the onlne socal footprnt poses n dentty theft and account password reset attacks. In contnuaton to ths lne of work, our work nvestgates the factors that contrbute to ncreased onlne socal footprnt, based on a cross-osn analyss of the nformaton avalable n publc profles. Our work provdes a novel evaluaton of the complementarty, consstency, and qualty of nformaton, whch are mportant factors when consderng cross-lnkng between OSNs and wth other data sources. De-anonymzaton of publcly released databases by combnng multple nformaton sources was studed n several works [1, 11]. Also, the smlarty of user names has been explored n [12], showng how easy t may be for a thrd party to guess an account name and gan access to a user account. Our work demonstrates crosslnkng wth a dfferent type of data source, whch s publcly avalable n a large number of countres and s not anonymous. As part of nvestgatng prvacy trends, Dey et. al. [4] characterzed OSN nformaton for New York Cty users based on varous demographcs, ncludng age and gender. Our work consders a larger set of OSNs and provdes an analyss based on several demographcs, ncludng occupaton and the use of pseudonyms, whch were not consdered before. 7. CONCLUSION Ths paper presents an analyss of nformaton revealed by lnkng the publc profles of people who use multple OSNs and the potental mplcatons on the user s prvacy from combnng ths nformaton wth another publcly avalable data source, the onlne phone drectory. Our most nterestng fndngs hghlght the hgh level of consstency and complementarty of user provded nformaton on OSNs and llustrate how aggregaton of nformaton over multple OSNs may make the retreval of addtonal personal nformaton from other publc sources easer. 8. ACKNOWLEDGEMENT Ths research work has been supported by fundng from Natonal ICT Australa (NICTA). 9. REFERENCES [1] L. Backstrom, C. Dwork, and J. Klenberg. Wherefore Art thou r379x?: Anonymzed Socal Networks, Hdden Patterns, and Structural Steganography. In WWW, pages ACM, 27. [2] T. Chen, R. Borel, M.-A. Kaafar, and A. Fredman. An analyss of socal footprnts across multple onlne socal networks. Techncal report, NICTA, March 212. [3] S. S. Cho, S. H. Cha, and C. Tappert. A Survey of Bnary Smlarty and Dstance Measures. Journal on Systemcs, Cybernetcs and Informatcs, 21. [4] R. Dey, Z. Jelveh, and K. W. Ross. Users Have Become Much More Prvate: A Large-Scale Study. In 4th IEEE Internatonal Workshop on Securty and Socal Networkng, 212. [] D. Iran, S. Webb, K. L, and C. Pu. Large Onlne Socal Footprnts An Emergng Threat. In Proceedngs of the 29 Internatonal Conference on Computatonal Scence and Engneerng, 29. [] D. Iran, S. Webb, C. Pu, and K. L. Modelng Unntended Personal-Informaton Leakage from Multple Onlne Socal Networks. IEEE Internet Computng, 1, May 211. [7] M. A. Jaro. Probablstc Lnkage of Large Publc Health Data Fles. Stat Med, 199. [8] B. Krshnamurthy and C. Wlls. On the Leakage of Personally Identfable Informaton va Onlne Socal Networks. ACM SIGCOMM Computer Communcaton Revew, 4: , 21. [9] B. Krshnamurthy and C. E. Wlls. Prvacy leakage n moble onlne socal networks. In Proceedngs of the 3rd conference on Onlne socal networks, 21. [1] MobleReference. Trgonometry Quck Study Gude for Smartphones and Moble Devces. MobleReference, 27. [11] A. Narayanan and V. Shmatkov. Robust De-anonymzaton of Large Sparse Datasets. IEEE Symposum on Securty and Prvacy, 28. [12] D. Perto, C. Castellucca, M. A. Kaafar, and P. Manls. How Unque and Traceable Are Usernames? In PETS,