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	<h2 style="color: #000000"> DTL Grantees 2015</h2>
	<div class="divider-line"></div>
<div class="grantees-intro">
	<p>

The DTL Grants support research in tools, data, platforms and 
methodologies for shedding light on the use of personal data by online 
services, and to empower users to be in control of their personal data 
online.</p> 
<p>The winners of the first DTL research grants are listed below. Each project received a lump sum of Euros 50,000. Click <a href="index.html#grants">here</a> for more information on the call for proposals. </p>
<p>The remaining proposals that made it to the top-third of all 
proposals have been awarded with a platform to present their work at the
 DTL2015 Conference, with a corresponding travel grant.<a href="#mentions"> Click here to view these proposals.</a></p>
 </div>
	
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			<h2>Providing Users Data-Driven Privacy Awareness Revealing and Controlling Mobile Privacy Leaks<a name="ur"></a></h2>   
<h6>Lorrie Faith Cranor (Carnegie Mellon University),Blase Ur (Carnegie Mellon University)</h6>
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						<p>Online behavioral advertising (OBA), the targeting of 
advertisements based on a user's web browsing, remains a major source of
 privacy invasion. Although a number of privacy tools (e.g., Ghostery, 
Lightbeam, and Privacy Badger) can help users control OBA, average users
 are left utterly confused about OBA even after using such tools. We 
propose moving beyond existing tools, which alert users to tracking 
occurring at the current moment, by designing and testing a tool that 
takes a data-driven, personalized approach to privacy awareness. We 
hypothesize that users can better understand OBA and resultant privacy 
threats if equipped with a tool that visualizes instances of them being 
tracked over time.</p> 
						
						<p>
						We will build and test such a data-driven privacy tool that 
enables users to explore on precisely which webpages different companies
 have tracked them, as well as what those companies may have inferred 
about their interests. Studies have shown benefits in notifying users 
about the collection of data by smartphone apps. Our proposal translates
 these insights to the OBA domain, yet makes further intellectual 
contributions by exploring the impact of presenting different 
abstractions and granularities of the information tracked (e.g., showing
 "Doubleclick knows you visited the following 82 pages" versus 
"Doubleclick has likely concluded that you like 'European travel' based 
on your visits to these 82 pages"). In addition to releasing our privacy
 tool as a fully functional, open-source project, we will conduct a 
75-participant, 2-week field trial comparing visualizations of 
personalized tracking data.</p>


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			<h2>Revealing and Controlling Mobile Privacy Leaks<a name="choff"></a></h2>   
<h6>David Choffnes (Northeastern University), Christo Wilson (Northeastern University), Alan Mislove (Northeastern University)</h6>

			</div>

			<div class="col-md-5 text-grid-b">		<p>The combination of rich 
sensors and ubiquitous connectivity make mobile devices perfect vectors 
for invading the privacy of end users. We argue that improving privacy 
in this environment requires trusted third-party systems that enable 
auditing and control over PII leaks. However, previous attempts to 
address PII leaks fall short of enabling such auditing and control 
because they face challenges of a lack of visibility into network 
traffic generated by mobile devices and the inability to control the 
traffic.</p>

<p>The proposed research will enable the auditing and control of PII 
leaks in network traffic from mobile devices using indirection to 
improve visibility and control for PII leaks in mobile network traffic. 
Specifically, we use natively supported mobile OS features to redirect 
all of a device’s Internet traffic to a trusted server to identify and 
control privacy leaks in network traffic.</p>
<p>
We will address the key challenges of how to identify and control PII 
leaks when users’ PII is not known a priori, nor is the set of apps that
 leak this information. First, to enable auditing through improved 
transparency, we will investigate how to use machine learning to 
reliably identify PII from network flows, and identify algorithms that 
incorporate user feedback to adapt to the changing landscape of privacy 
leaks. Second, we will build tools that allow users to control how their
 information is (or not) shared with second and third parties. These 
tools will be deployed as free, open-source applications that can run in
 a number of deployment scenarios, including on a device in a user’s 
home network, or in a shared cloud-based VM environment.
</p> 
						
					


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			<h2>FDVT: PERSONAL DATA VALUATION TOOL FOR FACEBOOK USERS</h2>   
<h6>Angel Cuevas (Universidad Carlos III de Madrid, co-PI), Ruben Cuevas
 (Universidad Carlos III de Madrid, co-PI)  Raquel Aparicio (Universidad
 Carlos III de Madrid)</h6>

			</div>

			<div class="col-md-5 text-grid-b">		<p>
A recent report of the Interactive Advertising Bureau revealed that 
online advertising generated in 2014 $49.5B worth of revenue only in US,
 representing an increase of 16% with respect to 2013, which in turn 
exceeded 17% the revenue of 2012. A great advantage of online 
advertising over more traditional printed and TV advertising is its 
capability to target individuals with specialized advertisements 
tailored to their personal information. For instance the ad campaign 
planner from Facebook (FB) allows defining an audience using more than 
13 different attributes related to personal information of the end-user.
 Therefore, an online advertiser can launch a campaign targeting a 
well-defined audience based on personal information attributes, thus an 
important part of the FB business model is built up on top of the 
personal information of its subscribers. Although there are no doubts of
 the legality of the business model implemented by FB and other major 
players in the Internet, there are some actors raising the request of 
generating tools that let end-users knowing what is the actual value of 
their personal information. In other words, how much money FB, Google, 
and other companies in the on-line advertising market make out of my 
personal information. Providing Internet users with simple and 
transparent tools that inform them of what is the value that their 
personal data generates is not only a civil society request, but a 
demand from governmental forces.</p> 
						
						<p>The goal of this project is to develop a tool that informs in 
real-time Internet end users regarding the economic value that the 
personal information associated to their browsing activity has 
generated. Due to the complexity of the problem we narrow down the scope
 of this tool to FB in this project, i.e., inform FB users in real time 
of the value that they are generating to FB. We refer to this tool as FB
 Data Valuation Tool (FDVT).
						 </p>


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			<h2>Digital Halo: Browsing History Awareness Revealing and Controlling Mobile Privacy Leaks</h2>   
<h6>Arkadiusz Stopczynski (Department of Applied Mathematics and 
Computer Science, Technical University of Denmark), Mieszko Piotr 
Manijak (Department of Applied Mathematics and Computer Science, 
Technical University of Denmark), Piotr Sapiezynski (Department of 
Applied Mathematics and Computer Science, Technical University of 
Denmark), Sune Lehmann (Department of Applied Mathematics and Computer 
Science, Technical University of Denmark)</h6>

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				<p>Our online browsing history is intensely personal. Our search 
terms and the web-pages we visit, reveal our fears, interests, 
illnesses, and secret ambitions. While many people are familiar with the
 concept of behavior-tracking and cookies, there is significantly less 
public awareness of just how personal our online behavior is. </p> 
						
						<p>A few years ago, the immersion project originating at the MIT 
Media Lab received world-wide press coverage by visualizing the latent 
social information contained in our email header information. We aim to 
do something similar for web-browsing. Using topic models, we aim to 
design a simple dashboard that allows individuals to visualize the 
content of their browsing, and observe how these topics change over 
time. Crucially, we will combine this visualization with information on 
data trackers (how many tracking parties, how much outgoing 
information), thus allowing users to directly observe what the data 
tracking means for them. 
						 </p>
				<p>
				Collected, as well as computed data, will be stored in safe, 
individualized ‘vaults’ in a storage system following the OpenPDS 
framework specification. Ipso facto ensuring strict sovereignty of users
 over their data.
				</p>

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			<h2>Estimating &amp; Controlling Personal Information Spread with Appu </h2>   
<h6>Yogesh Mundada (Princeton University), Nick Feamster (Princeton University), Sarthak Grover (Princeton University)</h6>

			</div>

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					<p>Personal information loss has been a worrisome issue for both 
researchers as well as regular users. Even though a lot of research 
has been done in security as well as privacy community, a personalized 
solution, addressing problems in both areas, which is useful to end 
users is missing. In this work, we present Appu, a browser extension, 
that automatically detects i) sensitive information of the user, ii) 
whether it is sufficiently secured, and iii) if it is getting leaked 
to third party domains. </p>

<p>To automatically detect users sensitive information, we developed 
scripting language to scrape this information from user’s existing 
accounts. Once the personal information store is populated with this 
information, Appu monitors user’s interaction with various accounts 
passively to detect further information spread. Appu also monitors 
whether any personal information is leaked to third parties. 
Over time, Appu presents the user a complete picture of personal 
information spread across the web. Appu also nudges the user to secure 
important but inadequately protected accounts. </p> 


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<div class="circle">PLATFORMS</div>

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			<h2>Reverse-engineering online tracking: From niche research field to easy-to-use tool<a name="engle"></a></h2>   
<h6>Arvind Narayanan (Princeton University), Steven Englehardt (Princeton University)</h6>

			</div>

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				<p>The third-party online tracking ecosystem lacks transparency 
about (1) which companies track users, (2) what user data is being 
collected, (3) what technologies are being used for tracking, and (4) 
data flows between trackers. Automated measurement can enable 
transparency and has already resulted in greater privacy awareness, 
improved privacy tools, and, at times, regulatory enforcement actions.</p>

<p>At Princeton we have built OpenWPM, a platform for online tracking 
transparency. We have used it in several published studies to detect and
 reverse-engineer online tracking. We now aim to democratize web privacy
 measurement: transform it from a niche research field to a widely 
available tool. We will do this in two steps: use OpenWPM to publish a 
"web privacy census" — a monthly web-scale measurement of tracking and 
privacy, comprising 1 million sites. The census will detect and measure 
many or most of the types of known privacy violations reported by 
researchers so far: circumvention of cookie blocking, leakage of PII to 
third parties, canvas fingerprinting, and more. Second, we will build an
 analysis platform to allow anyone to analyze the census data with 
minimal expertise. The platform will have "1-click reproducibility'" 
which will allow packaging and distributing study data, scripts, and 
results in a format that's easy to replicate and extend. </p> 



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	<section id="mentions" class="section2">
<h2 class="mention-header" style="color: #000000"> Mentions and Travel Grants</h2>
<div class="grantees-intro">
			<p>
		The following proposals are amongst the 
		top-third of all proposals and were offered a platform to 
		share their ideas and work with other members of the DTL community. 
They were offered a presentation slot at the forthcoming DTL workshop in
 November 2015 (location and details will be announced in due 
		time) as well as a travel grant to attend it. 
			</p> 
 </div>


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			<h2> A Deep learning platform for the reverse-engineering of
              Behavioral Targeting procedures in online ad networks
              (DeepBET)</h2>   
<h6>Sotirios Chatzis (Cyprus University of Technology), Aristodemos Paphitis (Cyprus University of Technology)</h6>
			</div>

			<div class="col-md-5 text-grid-c">	<p>Online ad networks are a 
characteristic example of online services that massively leverage user 
data for the purposes of behavioral targeting. A significant problem of 
these technologies is their lack of transparency. For this reason, the 
problem of reverse-engineering the behavioral targeting mechanisms of ad
 networks has recently attracted significant research interest. Existing
 approaches query ad networks using artificial user profiles, each of 
which pertains to a single user category. Nevertheless, well-designed ad
 services may not rely on such simple user categorizations: A user 
assigned to multiple categories may be presented with a set of ads quite
 different from the union of the set of ads pertaining to each one of 
their individual interests. Even more importantly, user interests may 
change or vary over time. Nevertheless, none of the existing 
reverse-engineering systems are capable of determining whether and how 
ad network targeting mechanisms adapt to such temporal dynamics.</p> 
						
						<p>The goal of this proposal is to develop a platform addressing 
these inadequacies by leveraging advanced machine learning methods. The 
proposed platform is capable of: 
(i) Intelligently creating a diverse set of (interest-based) user 
profiles to query ad networks with. It ensures that the (artificial) 
user profiles used to query the analyzed ad networks correspond to as 
diverse a set of combinations of user interests (characteristics) as 
possible. 
(ii) Obviating the need to rely on some publicly available tree of 
categories/user interests, as this can be restrictive to the analysis or
 even misleading. Instead, our platform is capable of reliably producing
 a tree-like content-based grouping (clustering) of websites into 
interest groups, in a completely unsupervised manner. 
(iii) Performing inference of the correlations between user 
characteristics and ad network outputs in a way that allows for large 
scale generalization.
(iv) Determining whether and how temporal dynamics affect these 
correlations, and on how long temporal horizons.</p>


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			<h2>Alibi: Turning User Tracking Into a User Benefit</h2>   
<h6>Marcel Flores, Andrew Kahn, Marc Warrior, Aleksandar Kuzmanovic (PI) (Northwestern University)</h6>

			</div>

			<div class="col-md-5 text-grid-c">		<p>We propose Alibi, a system 
that enables users to take direct advantage of the work online trackers 
do to record and interpret their behavior. The key idea is to use the 
readily available personalized content, generated by online trackers in 
real-time, as a means to verify an online user in a seamless and 
privacy-preserving manner. We propose to utilize such tracker-generated 
personalized content, submitted directly by the user, to construct a 
multi-tracker user-vector representation and use it in various online 
verification scenarios. The main research objectives of this project are
 to explore the fundamental properties of such user-vector 
representations, i.e., their construction, uniqueness, persistency, 
resilience, utility in online verification, etc. The key goal of this 
project is to design, implement, and evaluate the Alibi service, and 
make it publicly available.</p>
			</div> 

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			<h2> Towards Making Systems Forget </h2>   
<h6> Yinzhi Cao (Lehigh University and Columbia University)</h6>

			</div>

			<div class="col-md-5 text-grid-c">		<p>Today’s systems produce a 
rapidly exploding amount of data, and the data further derives more 
data, forming a complex data propagation network that we call the data’s
 lineage. There are many reasons that users want systems to forget 
certain data including its lineage. From a privacy perspective, users 
who become concerned with new privacy risks of a system often want the 
system to forget their data and lineage. From a security perspective, if
 an attacker pollutes an anomaly detector by injecting manually crafted 
data into the training data set, the detector must forget the injected 
data to regain security. From a usability perspective, a user can remove
 noise and incorrect entries so that a recommendation engine gives 
useful recommendations. Therefore, we envision forgetting systems, 
capable of forgetting certain data and their lineages, completely and 
quickly.
</p> 
						
						<p>
						In this proposal, we focus on making learning systems forget, the 
process of which we call machine unlearning, or simply unlearning. We 
present a general, efficient unlearning approach by transforming 
learning algorithms used by a system into a summation form. To forget a 
training data sample, our approach simply updates a small number of 
summations – asymptotically faster than retraining from scratch. Our 
approach is general, because the summation form is from the statistical 
query learning in which many machine learning algorithms can be 
implemented. Our approach also applies to all stages of machine 
learning, including feature selection and modeling.
						 </p>

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			<h2>Bringing Fairness and Transparency to Mobile On-Demand
              Services</h2>   
<h6>Christo Wilson (Northeastern University),
              Dave Choffnes (Northeastern University),
              Alan Mislove (Northeastern University)</h6>

			</div>

			<div class="col-md-5 text-grid-c">		
				<p> In this project, we aim to bring greater transparency to 
algorithmic pricing implemented by mobile, on-demand services. 
Algorithmic pricing was pioneered in this space by Uber in the form of 
"surge pricing". While we applaud mobile, on-demand services for 
disrupting incumbents and stimulating moribund sectors of the economy, 
we also believe that the data and algorithms leveraged by these services
 should be transparent. Fundamentally, consumers and providers cannot 
make informed choices when marketplaces are opaque. Furthermore, 
black-box services are vulnerable to exploitation once their algorithms 
are understood, which creates opportunities for customers and providers 
to manipulate these services in ways that are not possible in 
transparent markets.</p> 
						
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			<h2>Providing Users With Feedback on Search Personalised Learning</h2>   
<h6> Douglas Leith (Trinity College Dublin),    Alessandro Checco (Trinity College Dublin) </h6>

			</div>

			<div class="col-md-5 text-grid-c">
					<p>Users are currently given only very limited feedback from search
 providers as to what learning and inference of personal preferences is 
taking place.   When a search engine infers that a particular 
advertising category is likely to be of interest to a user, and so more 
likely to generate click through and sales, it will tend to use this 
information when selecting which adverts to display.   This can be used 
to detect search engine learning via analysis of changes in the choice 
of displayed adverts and to inform the user of this learning.   In this 
project we will develop a browser plugin that provides such feedback, 
essentially by empowering the user via the kind of data analytic 
techniques used by the search engines themselves. </p> 

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			<h2> Zero-Knowledge Transparency: Safe Audit Tools for End Users
</h2>   
<h6>Maksym Gabielkov (INRIA, Columbia University), Larissa Navarro 
Passos de Araujo (Columbia University), Max Tucker Da Silva (Columbia 
University), Augustin Chaintreau (Columbia University)</h6>

			</div>

			<div class="col-md-5 text-grid-c">		
				<p>In principle, data transparency tools follow strict privacy 
guidelines to protect customers’ data while revealing how this data is
 being used by others. But those objectives are often at odds. To take a
 simple example, answering questions like which of my email caused this 
ad to appear brings user to the following dilemma: she can either enjoy 
(blindly) the (relative) privacy offered by a service like gmail, or if 
she decides to voice her concern, can alternatively propose her data to 
participate in a data-transparency experiment with various tools (e.g., 
Xray, AdFisher, Sunlight and other more specific ones). The later 
involves running the experiment herself entirely or providing the data 
in clear form to one of those tools run by a third party. Both increases
 privacy risks, because sensitive data are now being manipulated by 
other pieces of codes, sometimes under someone else’s control. That 
explains that all tools mentioned above, and in fact with almost no 
exception all transparency research so far is run and validated on 
synthetic data-sets that are by nature not sensitive.</p>

<p>Here, our goal is to formally define zero-knowledge transparency, to 
reconcile the two needs of being informed and being safe when it comes 
to our data usage, and experiment with tools that provide this dual 
protection. As in our prior research, we aim at generic tools, that 
address a broad range of scenarios with the same underlying concepts. 
The first architecture we propose leverages differential correlation, as
 used in Xray for multiple services, to show that this tool can be made 
privacy-preserving with an additional simple architectural layers. The 
second architecture we envision is way broader: it leverages data bank 
with interactive queries such as air-cloak to separately solve privacy 
and transparency. We believe that most data transparency tools will 
require a similar complement and experiment with the robustness of this 
solution in the face of scale and other challenges posed.</p> 
					
			</div> 

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			<h2>Privacy-aware ecosystem for data sharing</h2>   
<h6> Anna Monreale (Department of Computer Science, University of
              Pisa)</h6>
			</div>

			<div class="col-md-5 text-grid-c">	<p>Human and social data are an 
important source of knowledge useful for understanding human behaviour 
and for developing a wide range of user services. Unfortunately, this 
kind of data is sensitive, because people's activities described by 
these data may allow re-identification of individuals in a de-identified
 database and thus can potentially reveal intimate personal traits, such
 as religious or sexual preferences. Therefore, Data Providers, before 
sharing those data, must apply any sort of anonymization to lower the 
privacy risks, but they must be aware and capable of controlling also 
the data quality, since these two factors are often a tradeoff. This 
project proposes a framework to support the Data Provider in the privacy
 risk assessment of data to be shared. This framework measures both the 
empirical (not theoretical) privacy risk associated to users represented
 in the data and the data quality guaranteed only with users not at 
risk. It provides a mechanism allowing the exploration of a repertoire 
of possible data transformations with the aim of selecting one specific 
transformation that yields an adequate trade-off between data quality 
and privacy risk. The project will focus on mobility data studying the 
practical effectiveness of the framework over forms of mobility data 
required by specific knowledge-based services.</p> 
						
					
			</div> 

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			<h2>Exposing and Overcoming Privacy Leakage in Mobile Apps using
              Dynamic Profiles </h2>   
<h6>Z. Morley Mao (University of Michigan)</h6>

			</div>

			<div class="col-md-5 text-grid-c">		<p>In this proposal, we focus on 
designing the support to detect the leakage of personal data in the 
mobile app ecosystem through a novel approach of using dynamically 
generated user'€™s application profiles to track how sensitive data 
influence the content presented to the users and also to discover the 
violation of user privacy policies. For the former, we analyze how 
various types of content personalization based on information such as 
behavior, context or location, social graph can lead to potentially 
unwanted bias in the content. For the latter, we take a semantic based 
approach to translate the user privacy preference into enforceable 
syntax-based mechanisms. By leveraging the dynamically generated 
profiles that characterize the expected content customization, users can
 select a type of profile that satisfy user’s privacy policy or obtain
 data or access the online service through a collection of profiles. In 
summary, our work consists of both offline approaches for generating the
 knowledge of content customization based on the relevant pro- files and
 to characterize the privacy-related behavior in mobile apps, as well as
 the run-time enforcement support to satisfy user-expressed privacy 
policies.</p>

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			<h2>Detecting Accidental and Intentional PII Leakage from Modern
              Web Applications</h2>   
<h6>Nick Nikiforakis (Stony Brook University)</h6>

			</div>

			<div class="col-md-5 text-grid-c">		<p>The rise of extremely popular 
online services offered at no fiscal cost to users has given rise to a 
rich online ecosystem of third party trackers and online advertisers. 
While the majority of tracking involves the use of cookies and other 
technologies that do not, directly, expose a user's personally 
identifiable information (PII), past research has shown that PII leakage
 is all too common. Either due to poor programming practices (e.g. 
PII-carrying, GET-submitting forms) or due to intentional information 
leakage, a user’s PII often finds its way to the hands of third 
parties. In the cases where a user’s PII leaks towards third parties 
that already use cookies and other tracking technologies, the trackers 
have now the potential to identify the user, by name, as she browses on 
the web.</p>

<p>Despite the magnitude and the severity of the PII-leakage problem, 
there is, currently, a dearth of usable, privacy-enhancing technologies 
that detect and prevent PII leakage. To restore the control of users 
over their own personally identifiable information, we propose to 
design, implement, and evaluate LeakSentry, a browser extension that has
 the ability to identify leakage as that is happening and give users 
contextual information about the leakage as well as the power to allow 
it, or block it. Next to LeakSentry's stand-alone mode, users of 
LeakSentry will be able to opt-in to a crowd-wisdom program where they 
can learn from each other'€™s choices. In addition, LeakSentry will have
 the ability to report the location of PII leakage, enabling us to 
create a PII-leaking page observatory, which can both apply pressure to 
the websites that were caught red-handed, as well as navigate other 
users away from them.
 </p> 
						
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			<h2>Towards Transparent Privacy Practices: Facilitating
              Comparisons of Privacy Policies</h2>   
<h6>Ali Sunyaev (Department of Information Systems, University of
              CologneUniversity of Cologne),
              Tobias Dehling (Department of Information Systems, University
              of Cologne)</h6>

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				<p> 
					A central challenge of privacy policy design is the wicked nature 
of privacy policies: In essence, privacy policies are past responses of 
providers to future information requests of users regarding the privacy 
practices of online services. As a result, today’s privacy policies 
feature a large variety of contents and designs. This impedes data 
transparency, in particular, with respect to comparisons of privacy 
practices between providers. The main idea of this research proposal is 
to leverage tagging and crowdsourcing to facilitate comparisons of 
privacy policies in a provider-independent web application. Our research
 is relevant for data transparency research because it aims to improve 
the most prevalent tool for shedding light into the use of personal data
 by online services, that is, privacy policies. Redeeming the benefits 
offered by online environments while avoiding the perils is challenging,
 this research proposal makes this task easier by improving transparency
 of privacy practices. There have been numerous efforts to improve the 
utility of privacy policies that focus on reshaping the privacy policies
 offered by providers, for instance, changing the layout or enhancing 
visualization. The main innovation pursued in this research proposal is 
that we do not focus on getting providers to publish better privacy 
policies, but instead focus on enabling users to make the best out of 
the privacy policies providers confront them with.
				</p> 
						
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			<h2> Improving the Comprehension of Browser Privacy Modes</h2>   
<h6>Sascha Fahl (DCSec, Leibniz Universität Hannover),
              Yasemin Acar (DCSec, Leibniz Universität Hannover),
              Matthew Smith (Rheinische Friedrich-Wilhelms-Universität
              Bonn)  </h6>

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					<p>  
					Online privacy is an important, hotly researched and demanded topic
 that gained even more relevance recently. However, existing mechanisms 
that protect users’ privacy online, such as TOR and using VPN 
connections are complex, bring performance issues with them and, in case
 of the latter, add costs. Therefore, their widespread use is not 
applicable for the public. Browser vendors have recently established 
so-called private browsing modes that are largely misunderstood by 
users: They over-rate the level of protection offered by the services, 
which can lead to insecure behaviour. We aim to study user 
misconceptions, enhance their comprehension and scientifically evaluate 
the usability and applicability of more privacy-enhancing services such 
as TOR. </p> 			

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			<h2>PRIVA­SEE: PRIVacy Aware visual SEnsitivity Evaluator</h2>   
<h6>Bruno Lepri (Fondazione Bruno Kessler),
              Elisa Ricci (Fondazione Bruno Kessler),
              Lorenzo Porzi (Fondazione Bruno Kessler)</h6>

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				<p>​Digitally sharing our lives with others is a captivating and 
often addictive activity. Nowadays 1.8 billion photos are shared daily 
on social media. These images hold a wealth of personal information, 
ripe for exploitation by tailored advertising business models, but 
placed in the wrong hands this data can lead to disaster. In this 
project, we want to see how the increasing of a person’s awareness about
 potential personal data sensitivity issues influences their decisions 
about what and how to share, and moreover, how valuable they perceive 
their personal data to be. To achieve this ambitious goal we aim to (i) 
develop a novel methodology, applied within a mobile app, to inform 
users about the potential sensitivity of their images. Sensitivity will 
be modeled by exploiting automatic inferences coming from advanced 
computer vision and deep learning algorithms applied to personal photos 
and associated metadata; (ii) perform user-­centric studies within a 
living-lab environment to assess how users’ posting behaviours and 
monetary valuation of mobile personal data are influenced by user 
awareness about content sharing risks.  </p> 
						
						


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			<h2>Engineering Systems for the 
Privacy-Conscientious Use of Online Metadata</h2>   
<h6>Yves-Alexandre de Montjoye (MIT)
              Latanya Sweeney (Harvard)</h6>

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				<p> Project description pending.</p> 
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			<h2>Bringing Transparency to Targeted Advertising</h2>   
<h6>Patrick Loiseau (EURECOM), Oana Goga (MPI-SWS)
</h6>

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				<p>Targeted advertising largely contributes to the support of free 
web services. However, it is also increasingly raising concerns from 
users, mainly due to its lack of transparency. The objective of this 
proposal is to increase the transparency of targeted advertising from 
the user’s point of view by providing users with a tool to understand 
why they are targeted with a particular ad and to infer what information
 the ad engines possibly have about them. Concretely, we propose to 
build a browser plugin that collects the ads shown to a user and 
provides her with analytics about these ads.</p> 
						
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			<h2>Exploring Personal Data on the Databox</h2>   
<h6>Hamed Haddadi (QMUL)</h6>

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				<p>We are in a ‘personal data gold rush’ driven by advertising being
 the primary revenue source for most online companies. These companies 
accumulate extensive personal data about individuals with minimal 
concern for us, the subjects of this process. This can cause many harms:
 privacy infringement, personal and professional embarrassment, 
restricted access to
				labour markets, restricted access to best value pricing, and many 
others. There is a critical need to provide technologies that enable 
alternative practices, so that individuals can participate in the 
collection, management and consumption of their personal data.We are 
developing the Databox, a personal networked device (and associated 
services) that collates and mediates access to personal data, allowing 
us to recover control of our online lives. We hope the Databox is a 
first step to re-balancing power between us, the data subjects, and the 
corporations that collect and use our data.</p> 
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	</section>