3,585 research outputs found
StoryDroid: Automated Generation of Storyboard for Android Apps
Mobile apps are now ubiquitous. Before developing a new app, the development
team usually endeavors painstaking efforts to review many existing apps with
similar purposes. The review process is crucial in the sense that it reduces
market risks and provides inspiration for app development. However, manual
exploration of hundreds of existing apps by different roles (e.g., product
manager, UI/UX designer, developer) in a development team can be ineffective.
For example, it is difficult to completely explore all the functionalities of
the app in a short period of time. Inspired by the conception of storyboard in
movie production, we propose a system, StoryDroid, to automatically generate
the storyboard for Android apps, and assist different roles to review apps
efficiently. Specifically, StoryDroid extracts the activity transition graph
and leverages static analysis techniques to render UI pages to visualize the
storyboard with the rendered pages. The mapping relations between UI pages and
the corresponding implementation code (e.g., layout code, activity code, and
method hierarchy) are also provided to users. Our comprehensive experiments
unveil that StoryDroid is effective and indeed useful to assist app
development. The outputs of StoryDroid enable several potential applications,
such as the recommendation of UI design and layout code
Link Prediction by De-anonymization: How We Won the Kaggle Social Network Challenge
This paper describes the winning entry to the IJCNN 2011 Social Network
Challenge run by Kaggle.com. The goal of the contest was to promote research on
real-world link prediction, and the dataset was a graph obtained by crawling
the popular Flickr social photo sharing website, with user identities scrubbed.
By de-anonymizing much of the competition test set using our own Flickr crawl,
we were able to effectively game the competition. Our attack represents a new
application of de-anonymization to gaming machine learning contests, suggesting
changes in how future competitions should be run.
We introduce a new simulated annealing-based weighted graph matching
algorithm for the seeding step of de-anonymization. We also show how to combine
de-anonymization with link prediction---the latter is required to achieve good
performance on the portion of the test set not de-anonymized---for example by
training the predictor on the de-anonymized portion of the test set, and
combining probabilistic predictions from de-anonymization and link prediction.Comment: 11 pages, 13 figures; submitted to IJCNN'201
DP-LTOD: Differential Privacy Latent Trajectory Community Discovering Services over Location-Based Social Networks
IEEE Community detection for Location-based Social Networks (LBSNs) has been received great attention mainly in the field of large-scale Wireless Communication Networks. In this paper, we present a Differential Privacy Latent Trajectory cOmmunity Discovering (DP-LTOD) scheme, which obfuscates original trajectory sequences into differential privacy-guaranteed trajectory sequences for trajectory privacy-preserving, and discovers latent trajectory communities through clustering the uploaded trajectory sequences. Different with traditional trajectory privacy-preserving methods, we first partition original trajectory sequence into different segments. Then, the suitable locations and segments are selected to constitute obfuscated trajectory sequence. Specifically, we formulate the trajectory obfuscation problem to select an optimal trajectory sequence which has the smallest difference with original trajectory sequence. In order to prevent privacy leakage, we add Laplace noise and exponential noise to the outputs during the stages of location obfuscation matrix generation and trajectory sequence function generation, respectively. Through formal privacy analysis,we prove that DP-LTOD scheme can guarantee \epsilon-differential private. Moreover, we develop a trajectory clustering algorithm to classify the trajectories into different kinds of clusters according to semantic distance and geographical distance. Extensive experiments on two real-world datasets illustrate that our DP-LTOD scheme can not only discover latent trajectory communities, but also protect user privacy from leaking
Measuring Membership Privacy on Aggregate Location Time-Series
While location data is extremely valuable for various applications,
disclosing it prompts serious threats to individuals' privacy. To limit such
concerns, organizations often provide analysts with aggregate time-series that
indicate, e.g., how many people are in a location at a time interval, rather
than raw individual traces. In this paper, we perform a measurement study to
understand Membership Inference Attacks (MIAs) on aggregate location
time-series, where an adversary tries to infer whether a specific user
contributed to the aggregates.
We find that the volume of contributed data, as well as the regularity and
particularity of users' mobility patterns, play a crucial role in the attack's
success. We experiment with a wide range of defenses based on generalization,
hiding, and perturbation, and evaluate their ability to thwart the attack
vis-a-vis the utility loss they introduce for various mobility analytics tasks.
Our results show that some defenses fail across the board, while others work
for specific tasks on aggregate location time-series. For instance, suppressing
small counts can be used for ranking hotspots, data generalization for
forecasting traffic, hotspot discovery, and map inference, while sampling is
effective for location labeling and anomaly detection when the dataset is
sparse. Differentially private techniques provide reasonable accuracy only in
very specific settings, e.g., discovering hotspots and forecasting their
traffic, and more so when using weaker privacy notions like crowd-blending
privacy. Overall, our measurements show that there does not exist a unique
generic defense that can preserve the utility of the analytics for arbitrary
applications, and provide useful insights regarding the disclosure of sanitized
aggregate location time-series
A Taxonomy for and Analysis of Anonymous Communications Networks
Any entity operating in cyberspace is susceptible to debilitating attacks. With cyber attacks intended to gather intelligence and disrupt communications rapidly replacing the threat of conventional and nuclear attacks, a new age of warfare is at hand. In 2003, the United States acknowledged that the speed and anonymity of cyber attacks makes distinguishing among the actions of terrorists, criminals, and nation states difficult. Even President Obama’s Cybersecurity Chief-elect recognizes the challenge of increasingly sophisticated cyber attacks. Now through April 2009, the White House is reviewing federal cyber initiatives to protect US citizen privacy rights. Indeed, the rising quantity and ubiquity of new surveillance technologies in cyberspace enables instant, undetectable, and unsolicited information collection about entities. Hence, anonymity and privacy are becoming increasingly important issues. Anonymization enables entities to protect their data and systems from a diverse set of cyber attacks and preserves privacy. This research provides a systematic analysis of anonymity degradation, preservation and elimination in cyberspace to enhance the security of information assets. This includes discovery/obfuscation of identities and actions of/from potential adversaries. First, novel taxonomies are developed for classifying and comparing well-established anonymous networking protocols. These expand the classical definition of anonymity and capture the peer-to-peer and mobile ad hoc anonymous protocol family relationships. Second, a unique synthesis of state-of-the-art anonymity metrics is provided. This significantly aids an entity’s ability to reliably measure changing anonymity levels; thereby, increasing their ability to defend against cyber attacks. Finally, a novel epistemic-based mathematical model is created to characterize how an adversary reasons with knowledge to degrade anonymity. This offers multiple anonymity property representations and well-defined logical proofs to ensure the accuracy and correctness of current and future anonymous network protocol design
Empirical Analysis of Privacy Preservation Models for Cyber Physical Deployments from a Pragmatic Perspective
The difficulty of privacy protection in cyber-physical installations encompasses several sectors and calls for methods like encryption, hashing, secure routing, obfuscation, and data exchange, among others. To create a privacy preservation model for cyber physical deployments, it is advised that data privacy, location privacy, temporal privacy, node privacy, route privacy, and other types of privacy be taken into account. Consideration must also be given to other types of privacy, such as temporal privacy. The computationally challenging process of incorporating these models into any wireless network also affects quality of service (QoS) variables including end-to-end latency, throughput, energy use, and packet delivery ratio. The best privacy models must be used by network designers and should have the least negative influence on these quality-of-service characteristics. The designers used common privacy models for the goal of protecting cyber-physical infrastructure in order to achieve this. The limitations of these installations' interconnection and interface-ability are not taken into account in this. As a result, even while network security has increased, the network's overall quality of service has dropped. The many state-of-the-art methods for preserving privacy in cyber-physical deployments without compromising their performance in terms of quality of service are examined and analyzed in this research. Lowering the likelihood that such circumstances might arise is the aim of this investigation and review. These models are rated according to how much privacy they provide, how long it takes from start to finish to transfer data, how much energy they use, and how fast their networks are. In order to maximize privacy while maintaining a high degree of service performance, the comparison will assist network designers and researchers in selecting the optimal models for their particular deployments. Additionally, the author of this book offers a variety of tactics that, when used together, might improve each reader's performance. This study also provides a range of tried-and-true machine learning approaches that networks may take into account and examine in order to enhance their privacy performance
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