340 research outputs found
What Does The Crowd Say About You? Evaluating Aggregation-based Location Privacy
Information about people’s movements and the
locations they visit enables an increasing number of mobility
analytics applications, e.g., in the context of urban and transportation
planning, In this setting, rather than collecting or
sharing raw data, entities often use aggregation as a privacy
protection mechanism, aiming to hide individual users’ location
traces. Furthermore, to bound information leakage from
the aggregates, they can perturb the input of the aggregation
or its output to ensure that these are differentially private.
In this paper, we set to evaluate the impact of releasing aggregate
location time-series on the privacy of individuals contributing
to the aggregation. We introduce a framework allowing
us to reason about privacy against an adversary attempting
to predict users’ locations or recover their mobility patterns.
We formalize these attacks as inference problems, and
discuss a few strategies to model the adversary’s prior knowledge
based on the information she may have access to. We
then use the framework to quantify the privacy loss stemming
from aggregate location data, with and without the protection
of differential privacy, using two real-world mobility datasets.
We find that aggregates do leak information about individuals’
punctual locations and mobility profiles. The density of
the observations, as well as timing, play important roles, e.g.,
regular patterns during peak hours are better protected than
sporadic movements. Finally, our evaluation shows that both
output and input perturbation offer little additional protection,
unless they introduce large amounts of noise ultimately destroying
the utility of the data
On relational learning and discovery in social networks: a survey
The social networking scene has evolved tremendously over the years. It has grown in relational complexities that extend a vast presence onto popular social media platforms on the internet. With the advance of sentimental computing and social complexity, relationships which were once thought to be simple have now become multi-dimensional and widespread in the online scene. This explosion in the online social scene has attracted much research attention. The main aims of this work revolve around the knowledge discovery and datamining processes of these feature-rich relations. In this paper, we provide a survey of relational learning and discovery through popular social analysis of different structure types which are integral to applications within the emerging field of sentimental and affective computing. It is hoped that this contribution will add to the clarity of how social networks are analyzed with the latest groundbreaking methods and provide certain directions for future improvements
Evaluating Privacy-Friendly Mobility Analytics on Aggregate Location Data
Information about people's movements and the locations they visit enables a wide number of mobility analytics applications, e.g., real-time traffic maps or urban planning, aiming to improve quality of life in modern smart-cities. Alas, the availability of users' fine-grained location data reveals sensitive information about them such as home and work places, lifestyles, political or religious inclinations. In an attempt to mitigate this, aggregation is often employed as a strategy that allows analytics and machine learning tasks while protecting the privacy of individual users' location traces. In this thesis, we perform an end-to-end evaluation of crowdsourced privacy-friendly location aggregation aiming to understand its usefulness for analytics as well as its privacy implications towards users who contribute their data. First, we present a time-series methodology which, along with privacy-friendly crowdsourcing of aggregate locations, supports mobility analytics such as traffic forecasting and mobility anomaly detection. Next, we design quantification frameworks and methodologies that let us reason about the privacy loss stemming from the collection or release of aggregate location information against knowledgeable adversaries that aim to infer users' profiles, locations, or membership. We then utilize these frameworks to evaluate defenses ranging from generalization and hiding, to differential privacy, which can be employed to prevent inferences on aggregate location statistics, in terms of privacy protection as well as utility loss towards analytics tasks. Our results highlight that, while location aggregation is useful for mobility analytics, it is a weak privacy protection mechanism in this setting and that additional defenses can only protect privacy if some statistical utility is sacrificed. Overall, the tools presented in this thesis can be used by providers who desire to assess the quality of privacy protection before data release and its results have several implications about current location data practices and applications
Ensuring Application Specific Security, Privacy and Performance Goals in RFID Systems
Radio Frequency IDentification (RFID) is an automatic identification technology that uses radio frequency to identify objects. Securing RFID systems and providing privacy in RFID applications has been the focus of much academic work lately. To ensure universal acceptance of RFID technology, security and privacy issued must be addressed into the design of any RFID application. Due to the constraints on memory, power, storage capacity, and amount of logic on RFID devices, traditional public key based strong security mechanisms are unsuitable for them. Usually, low cost general authentication protocols are used to secure RFID systems. However, the generic authentication protocols provide relatively low performance for different types of RFID applications. We identified that each RFID application has unique research challenges and different performance bottlenecks based on the characteristics of the system. One strategy is to devise security protocols such that application specific goals are met and system specific performance requirements are maximized.
This dissertation aims to address the problem of devising application specific security protocols for current and next generation RFID systems so that in each application area maximum performance can be achieved and system specific goals are met. In this dissertation, we propose four different authentication techniques for RFID technologies, providing solutions to the following research issues: 1) detecting counterfeit as well as ensuring low response time in large scale RFID systems, 2) preserving privacy and maintaining scalability in RFID based healthcare systems, 3) ensuring security and survivability of Computational RFID (CRFID) networks, and 4) detecting missing WISP tags efficiently to ensure reliability of CRFID based system\u27s decision. The techniques presented in this dissertation achieve good levels of privacy, provide security, scale to large systems, and can be implemented on resource-constrained RFID devices
SoundCam: A Dataset for Finding Humans Using Room Acoustics
A room's acoustic properties are a product of the room's geometry, the
objects within the room, and their specific positions. A room's acoustic
properties can be characterized by its impulse response (RIR) between a source
and listener location, or roughly inferred from recordings of natural signals
present in the room. Variations in the positions of objects in a room can
effect measurable changes in the room's acoustic properties, as characterized
by the RIR. Existing datasets of RIRs either do not systematically vary
positions of objects in an environment, or they consist of only simulated RIRs.
We present SoundCam, the largest dataset of unique RIRs from in-the-wild rooms
publicly released to date. It includes 5,000 10-channel real-world measurements
of room impulse responses and 2,000 10-channel recordings of music in three
different rooms, including a controlled acoustic lab, an in-the-wild living
room, and a conference room, with different humans in positions throughout each
room. We show that these measurements can be used for interesting tasks, such
as detecting and identifying humans, and tracking their positions.Comment: In NeurIPS 2023 Datasets and Benchmarks Track. Project page:
https://masonlwang.com/soundcam/. Wang and Clarke contributed equally to this
wor
Privacy in trajectory micro-data publishing : a survey
We survey the literature on the privacy of trajectory micro-data, i.e.,
spatiotemporal information about the mobility of individuals, whose collection
is becoming increasingly simple and frequent thanks to emerging information and
communication technologies. The focus of our review is on privacy-preserving
data publishing (PPDP), i.e., the publication of databases of trajectory
micro-data that preserve the privacy of the monitored individuals. We classify
and present the literature of attacks against trajectory micro-data, as well as
solutions proposed to date for protecting databases from such attacks. This
paper serves as an introductory reading on a critical subject in an era of
growing awareness about privacy risks connected to digital services, and
provides insights into open problems and future directions for research.Comment: Accepted for publication at Transactions for Data Privac
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