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oader permissions than what are legitimately necessary for their services. Not mindful of the privacy implications, many users grant these permissions, bypassing this layer of protection as well. Thus, despite the mechanisms available to limit the exposure of information, OSN APIs can easily compromise users’ privacy. This paper presents a comparative analysis of the propensity of OSN APIs to violate user privacy. Ever since Facebook CEO Mark Zuckerberg famously defended the change to make user activity public-by-default, technologists have asserted that the “Age of Privacy” on the Web is over. 1 We seek to verify the truth in this assertion by examining the APIs of six popular and widely used OSNs. We f nd substantial support for this assertion because OSNs: (i) facilitate a collection of detailed user data; (ii) provide limited privacy settings and control; and (iii) use ambiguous requests to seek unnecessarily broad and general permissions. The paper is organized as follows: Section 2 introduces OSN APIs and privacy control mechanisms. Section 3 presents our comparative analysis. Section 4 summarizes our f ndings and offers directions for future work. 2 OSN APIs: An Overview Fundamentally, OSNs offer a platform for users to connect, interact, and share information. Each user is represented with a prof le that contains the user’s information. A social feed attached to the prof le provides a forum to share information. OSN users establish connections; and updates to their social feeds are broadcast to their connections. Popular OSNs differ with respect to what constitutes user connections and the information shared via social feeds. User connections on Facebook, Orkut, and Google+ are bidirectional friendships, social feeds include status updates, photos, videos and posts by other users, and updates can be broadcast to all or a subset of users’ connections. Twitter (YouTube) def nes uni-directional relationships where users subscribe to receive other users’ updates (video uploads). Each OSN designs an API to allow third parties to collect information or to act on behalf of their users. These open, Web-based APIs can be queried with HTTP requests to retrieve a particular user’s information. For example, the request https://graph.facebook.com/220439 retrieves all the public information on Facebook’s CTO. A request to http://search.twitter.com/search. json?q=happy returns recent tweets which include the word happy. Frequently, the APIs also return irrelevant information; for instance, the above Twitter call returns the name, user id, and a link to the user’s photo for each tweet. OSN users may expect that their shared information will be protected at the friendship, network, and public levels [6]. Friendship- and network-level protection prevents unintended disclosure, and users believe that these may be achieved through privacy settings. They may also presume 1 http://news.cnet.com/8301-17852 3-10431741-71.html Figure 1. API access and privacy protection public-level protection, because their notion of an OSN is a walled garden, impenetrable without authorization. Piercing through these privacy expectations, however, are the open access APIs. Most OSNs thus try to restore users’ perception of privacy by empowering them with two forms of control: (i) conf gurable privacy settings; and (ii) express permission requests from third parties. Understanding how the APIs of six popular OSNs balance these competing concerns of information access versus user privacy is the focus of this paper. We choose these OSNs because of their extreme popularity, widely varying purposes, and unique features as summarized in Table 1. We measure popularity in terms of the number of users 2 and include representative (not exhaustive) prof le data. 3 Comparative Analysis We extensively analyzed the online documentation, and aggregated the details of each OSN API along three dimensions: (i) user information that can be collected; (ii) privacy settings that can be conf gured; and (iii) permissions that may be requested. 3 For each dimension, we def ne the coverage metric as the percentage of the number of features that the API currently implements to the total number of features that it can feasibly provide, given the context and the functionality of the site. Because the total number of feasible features is determined by the union of the features across all APIs, it is inherently subject to our interpretation of the APIs. Therefore, we intend to use the coverage metric as a 2 http://vincos.it/social-media-statistics http://royal.pingdom.com/2011/02/04/facebook-youtube-our-collectivetime-sinks-stats 3 API details are available at http://www.cse.uconn.edu/∼ded02007/osnapi. 401
OSN Social Feed Unique Features Connections Prof le #Users Facebook Status, media, Groups, events, Bi-directional Name, contact info, 845M+ friend posts, notes, “Like” brands friendship family, education, external sites, and Web pages, work history, interests application alerts service integration Twitter Tweets, retweets, mention tweets Google+ Status, media, friend posts, external sites LinkedIn Status, resume updates, discussion posts, application alerts YouTube Uploaded, subscribed, and favorite videos Orkut Status, media, friend posts, application alerts Public access social feed Social circles, groups, “+1” brands and Web pages Groups, message boards, share professional recommendations Age, country, occupation, education, interests Share video channels and playlists, public commentary on videos Integrated applications Uni-directional subscriber Bi-directional friendship Bi-directional, professional connection Uni-directional subscriber Bi-directional friendship Real name, location 200M+ Name, contact info, education, work history, interests Name, work history, education, professional accomplishments, goals, skills Name, contact info, pets, family status, romance views, drinking habits Table 1. Purpose, popularity (# of users), and features of OSNs 90M+ 150M+ 490M+ 120M+ rough measure of the strength of an API along each dimension, and report their approximate rather than exact values. Next, we compare the APIs along these dimensions to understand their similarities and differences. 3.1 User information We def ne nine categories to homogenize the disparity in the types of data that users can upload as seen from Table 1, in order to meaningfully compare the OSN APIs. • Profile: Personal details such as hometown, date of birth, gender, and other information. • Social Message: Text posted to users’ social feeds, appearing in the news feeds of their connections. • Media: Photos, videos, and music. • Friend: First-degree connections. • Friend-of-friend: Second-degree connections. • Brand Page: A page dedicated to a branded entity, such as a company, a business, a movie, or a band. • Event: Event details such as name, place, and time. • Group: Membership details of users’ groups. • Note: A long post concerning a specif c subject; typically more detailed than a social message. The number of unique methods for each data type are: Prof le (94), Social Message (10), Media (23), Friend (23), Friend-of-Friend (21), Brand Page (7), Event (7), Group (5), and Note (7). Table 2 lists the number of methods and coverage for each OSN. It shows that because Google+ implements only 9 methods for Social Messages, although it could feasibly implement 10, its coverage for this data type is 90%. If an OSN does not provide any method for a given data type, a 0 appears in the corresponding cell in the upper table (Google+, Friend-of-friend). Additionally, if a OSN does not implement any of the feasible methods for a data type, a 0 appears in the corresponding cell in the lower table (Google+, Friend-of-friend). Finally, when no methods are feasible for a given data type for a given OSN, a 0 appears in the upper table, and a “−” appears in the lower table (YouTube, Events). 4 For each data type, the number of distinct methods is less than the total number of methods across all OSNs; 92 distinct methods collect prof le data, but a total of 209 prof le access methods exist across all OSNs. All APIs offer high coverage for prof le data. This is expected from Facebook, Orkut, and LinkedIn because their primary purpose is sharing of personal information. Surprisingly, YouTube makes many user details available, although its main objective is open, public sharing. Twitter, Facebook, and YouTube APIs can retrieve nearly every attribute of media and social messages, leading to very high coverage. Although Google+ does not provide access to media, this is planned. 5 Most APIs only expose names and prof le pictures (if any), of f rst- and second- degree connections. Ta- 4 Tables 3 and 5 have the same interpretation of 0 and -. 5 http://siliconf lter.com/google-gets-an-api-for-photos-and-videos/ 402
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SEKE San Francisco Bay July 1-3 201
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Copyright © 2012 by Knowledge Syst
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The 24 th International Conference
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Zhenyu Chen, Nanjing University, Ch
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Riccardo Martoglia, University of M
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Poster/Demo Sessions Co-Chairs Fars
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Evaluating the Cost-Effectiveness o
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Program Understanding Evaluating Op
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An Empirical Study of Execution-Dat
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Computer Forensics: Toward the Cons
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Modeling Bridging KDM and ASTM for
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A Mapping Study on Software Product
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A proposal for the improvement of t
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Panel on Future Trends of Software
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een available for analysis. Social
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The rest of this paper is orga nize
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Now we assume is given, we first pr
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“iphone” and “blackberry” a
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nique estimate the impact set based
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Figure 1. An example to illustrate
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trategy, the results should support
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Figure 1. System Architecture would
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Figure 2. Sequence Diagram of Train
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deployed on Android phone and runs
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the ontology information is only us
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Both kinds of axioms lead to an inh
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engineer and achieving requirements
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elicitation and the act ivities con
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Step 2. Step 3. 1.2. Use of Procedu
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Figure 6.b. Task: Cognitive Analysi
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original User’s Discourse / Speec
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II. RELATED WORK Business processes
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test, we did not find any significa
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For the indicator “requirements a
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the model checkers SPIN [8] and NuS
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allow for the presentation of syste
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the requirement or design phases sh
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using the CR estimates against the
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Data Set TABLE II. DATA SETS Artifa
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Figure 4. Relative Error in the Est
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Take basic requirements from user a
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sent Semantic Web Ontologies. It co
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The rest of the paper is organized
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and the risk level. Existing assess
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diagram could give developers an in
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Figure 2. Co
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• Sensor(Lexical): at the top and
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An Overview of the RSLingo Approach
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Figure 2. Overview of the RSLingo a
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DETECTING EMERGENT BEHAVIOR IN DIST
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Definition 2: For a set of MSCs M,
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Stability of Filter-Based Feature S
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Algorithm 1: Threshold-Based Featur
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MI KS GM AUC PRC S2N RUS35 RUS50 RO
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80
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82
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84
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86
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Cloud Application Resource Mapping
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Fig. 1. UML Collaboration Diagram m
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Fig. 2. UML Activity Diagram modeli
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An Empirical Study of Software Metr
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TABLE I SOFTWARE DATASETS CHARACTER
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1) Classifiers: In this study, soft
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Progressive Clustering with Learned
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IV. DATA SOURCES We have been worki
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meta-data we assign to indicate sig
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Multi-Objective Optimization of Fuz
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uilding FNNs that satisfy different
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VI. EXPERIMENTAL RESULTS The presen
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Generating Performance Test Scripts
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test duration(s) for the scenario(s
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which are composed by the name and
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A Catalog of Patterns for Concept L
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assisted solution for lattice analy
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The complete lattice of the class s
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Client-Side Rendering Mechanism: A
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JavaScript code directly within the
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Fig. 6. CPU usage percentage of pre
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may introduce the extra learning cu
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[9] but no validation is provided i
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One of the st udents from Group B t
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scribed by the equation: where η i
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4 Model Checking DPN We use HyTech
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Recommender systems are usually cla
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such as one week and gradually dete
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•
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146
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Enforcing Contracts for Aspect-orie
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Table 1. Behavioral Rules in LSD [9
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1 public privileged aspect Update {
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Towards More Generic Aspect-Oriente
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vals must be denoted by some notion
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Aspect-Orientation in the Developme
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Table II SELECTED PRIMARY STUDIES #
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Evaluating Open Source Reverse Engi
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Table 1. Candidate reverse engineer
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Some tools may perform the needed f
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Coordination Model to Support Visua
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this CMV approach we employee three
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colored according to the Gradient T
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Improving Program Comprehension in
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The method used to support and eval
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
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An Approach for Software Component
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properties. In particular, the foll
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Conference Dataset Anatomy Dataset
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equipment maintenance node may cont
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Online Anomaly Detection for Compon
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B. Monitoring Static Method Invocat
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undle which is responsible for pars
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An Exploratory Study of One-Use and
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Figure 1. Mindmap of Reuse Design P
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subject also caused outliers for re
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Choosing licenses in free open sour
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The important aspect is to provide
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that holds a model of each software
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Online Probability Application Char
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TABLE II THE PARAMETERS FOR THE HAR
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[10] N. Gunther, “Hit-and-run tac
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Section 5 d iscusses some related w
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model and the analysis results. The
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tree view of UML model, as show in
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II. BACKGROUND In this section, we
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Algorithm 2 MarkStatements function
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(a) printtok (b) printtok2 (c) sche
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Gupta extended HGS and proposed the
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manage intellectual knowledge with
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D. Manage Training When any trainin
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TABLE II. COMPARISON BETWEEN THE EX
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B. Trace Semantics of DAP Fig. 1. A
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∀ [DR ′ ] []gen [; ] [D]□ [;
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transformation idea in MDA [13] . T
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MappingRule MappingTGtoHP { [Rule I
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executing a continuous transition,
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protocol of DATS we used is properl
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in Boolean search and reasoning has
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to be broken by expelling one of it
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where, among other things: 1. The t
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A never-ending language learner cal
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4. i 2 Learning(DEC, mex) via Bias
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4.4. An Illustrative Example Now le
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Evolutionary Learning and Fuzzy Log
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In addition to the rules, the knowl
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As described in the previous sectio
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the ontology hierarchy; learning a
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three different learning techniques
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TABLE IX. USING SVM FOR LEARNING tr
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Siemens set contains seven C progra
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The program tcas is not included be
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esults. To be more specific, they w
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According to IEEE [10], regression
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(a) Volatility (b) Complexity (c) R
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It mimics the test engineer knowled
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integrate the test results. • Aut
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No tify the cloud controller to get
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of execution-data classification. T
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Figure 1. Boxplots of average AUC r
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anches is more applicable than the
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To validate the proposed approach,
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contains broadcast of Subject to Ob
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S 1a=1; S 2b=5; cobegin { S 3read
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.
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298
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A functionality is the ability of a
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level); in UML/J2EE technology [11]
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• to provide a unified catalogue
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flexibility they provide to model s
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complete in zero-time, and thus hav
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figure assumes the experiment with
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could be a practical way of dealing
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and (iv) the reduced amount of line
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III.BUSINESS RULES FRAMEWORK FOR KN
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explanation for why the program wan
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320
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322
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A model introducing SOAs quality at
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there exist a hierarchical ranking
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Software as a Service: Undo Hernán
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operations consist in reinjection i
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stored. If event failure, external
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ensure users the trustworthiness of
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Patient Doctor Doctor Patient Direc
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going down at a time. This is a rea
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Decidability of Minimal Supports of
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A S 1 1 1 0 0 0 1 1 0 0 0 1 1
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satisfies R( D) R( C) S 1. Compa
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Automated Generation of Concurrent
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target of testing. A blackbox test
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Using FCA-based Change Impact Analy
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Table 2. Impact set of C1,C2,C5 cha
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Figure 3. The PF and PTS results fo
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Forecasting Fault Events in Power D
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which the airport data did not cont
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classified. Recall is the probabili
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Testing Interoperability Security P
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Figure 2. Overview of the Test Gene
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Objective Figure 4. Testing
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A New Approach to Evaluate Path Fea
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k v ( df i ) 0 , it means that the
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Model & Method Feasibility Evaluati
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Structural Testing for Multithreade
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adequate to another criterion. This
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480
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482
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484
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A Tiny Specification Metalanguage W
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C. Syntax Extensions The expressive
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however, does not allow variables f
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derivation dependencies. The third
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model extension as proposed in [6].
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Figure 1. The process for engineeri
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Figure 4. The abstractions extracte
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way to detect something which could
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Algorithm 1: Algorithm to recursive
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obtain the input of experts on desi
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A. Pattern Reuse In patterns reuse
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IV. SCATTER In order to enhance pat
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These latter would be clustered acc
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described or even r etrieved, thus
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DC2AP Element and their Application
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21. Consequences Example of use DC2
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Legacy2Cloud logic. In this scope,
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2.3.1 Complementarity of MDD techno
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3.2.2 EAggregateRelationship The EA
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cess. The proposal follows the ADM
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II. OVERVIEW OF MODAL TRANSITION SY
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(1) V □ (A) ⊆ V □ (B), V ♦
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Suppose P = 〈S P , SP i , AI P ,
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Adaptive software should basically
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A. Step S1 - Define the Business Pr
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F. Step S6 - Monitor at Run-Time Th
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Figure 1, a metamodel defines an XM
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Figure 3 is a feature diagram that
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ecause the system of C-net model is
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Pub-T Tc Tc Sub-T Sub-T Figu
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Visual Studio Achievements, a Visua
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the project. This achievement has t
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proposed in this work requires the
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FTS is an important research area,
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C. Dependent Variables and Measures
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D. Conclusion Validity Conclusion v
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evaluation of team productivity, qu
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Figure 3: Process Fragment Example
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complexity issues traditionally inv
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Fig. 1: Swing Framework Class Diagr
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ize:Class”, with an empty precond
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Fig. 11: Overwritten Method Fig. 12
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Investigating the use of Bayesian n
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process for the network. Thus, this
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Reuse of Experiences Applied to Req
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Step 1: To start the process, the u
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Specification of Safety Critical Sy
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current belief of agents in order t
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Using the Results from a Systematic
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obtained from the categorization of
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that automatically presents the usa
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Improving a Web Usability Inspectio
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Following the experimental methodol
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About the category “Improvements
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Identification Guidelines for the D
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created according to the preview re
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In our analysis, the “Government
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In the following, we present the re
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USE SCENARIO The application Vuelos
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[8] Mayhew, D., “The Usability En
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Interaction State Set Structure Dat
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B. Component Model Fig. 2 show s th
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As a continuation of our research w
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PROMETHEE methods [3] belong to a f
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comparison rule, a value of 1/9 is
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TABLE V: Supermatrix for the exampl
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for knowledge sharing. Based on str
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y applying hash functions to its su
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Empirical Validation of Variability
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III. EXPERIMENTAL STUDY In this sec
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Table III SPEARMAN’S CORRELATION
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A Mapping Study on Software Product
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most relevant sources in software e
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TABLE IV TOOL’S CLASSIFICATION AC
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[2] P. Clements and L. Northrop, So
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and future works. II. BACKGROUND ON
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System Advanced Standard Module A M
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Algorithm 1: Backtracking for MIN-A
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outcomes from such research fields,
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TABLE IV. CONTINUED FROM PREVIOUS P
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contribution of each study was made
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assets, the plugin approach can be
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Figure 3: PlugSPL Feature Model Edi
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contributions from the several acto
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o Guideline 6.4: Softgoal dependenc
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descriptions must also b e configur
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It means that throughout the develo
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B. Instrumentation The experiment w
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• External Validity: W e have con
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II. THE PROPOSED TAXONOMY The taxon
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sub-dimension is categorized as, co
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A Proposal of Reference Architectur
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Figure. 1. Reference Architecture M
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A Variability Management Method for
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C. Correctness of configuration fil
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means patterns which are shown in s
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Tool Support for Anomaly Detection
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Figure 1. System overview of the SD
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Once the datasets were evaluated us
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Reconfiguration of Robot Applicatio
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data dependencies required for keep
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Spacemaker: Practical Formal Synthe
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Fig. 1. The ecommerce object model.
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Fig. 3. Mapping diagram for Figure
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A formal support for incremental be
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machine may be empty which should l
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software, based on revised requirem
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A Process-Based Approach to Improvi
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Appropriate processes m ust support
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Figure 2. Reordered layers of the k
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Automatic Acquisition of isA Relati
Page 737 and 738:
III. VALIDATING THE DIMENSION HEADE
Page 739 and 740:
The table title and the dimension s
Page 741 and 742:
A light weight alternative for OLAP
Page 743 and 744:
i.d. subsets of cubes focused on se
Page 745 and 746:
Match production rules Enterprise S
Page 747 and 748:
A Tool for Visualization of a Knowl
Page 749 and 750:
other ontology visualization tools
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shown at Figure 5. Objectives are s
Page 753 and 754:
Rendering UML Activity Diagrams as
Page 755 and 756:
a = O1 → a → O2 b = O2 → b
Page 757 and 758:
ecordProblem → A → reproducePro
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umlTUowl - A Both Generic and Vendo
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The tool’s ability to deal with S
Page 763 and 764:
Elem. UML Example D 12 Harmonizing
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4) Associations: In the first test
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III. GRAPH REPRESENTATION OF CLASS
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as an add-in to popular UML m odeli
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742
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744
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746
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her necessities. However, the progr
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Figure 3. Global Log. of terms. The
Page 781 and 782:
two stages. The first stage is focu
Page 783 and 784:
754
Page 785 and 786:
756
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758
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With the GDUC approach, we can mode
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Figure 6. Three proposals containin
Page 793 and 794:
764
Page 795 and 796:
766
Page 797 and 798:
Proactive Two Way Mobile Advertisem
Page 799 and 800:
information. If more than one adver
Page 801 and 802:
Users can al so publish adv ertisem
Page 803 and 804:
The COIN Platform: Supporting the M
Page 805 and 806:
A-3
Page 807 and 808:
Checking Contracts for AOP using XP
Page 809 and 810:
Maicon B. da Silveira, 112 Aldo Dag
Page 811 and 812:
Seyedehmehrnaz Mireslami, 70 Takao
Page 813 and 814:
Wei Zhang, 422 Wenbo Zhang, 188 Zhi
Page 815 and 816:
Desmond Greer Eric Gregoire Christi
Page 817 and 818:
Poster/Demo Presenter’s Index A A
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SEKE 2012 Proceedings - Knowledge Systems Institute

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