978 research outputs found
CYCLOSA: Decentralizing Private Web Search Through SGX-Based Browser Extensions
By regularly querying Web search engines, users (unconsciously) disclose
large amounts of their personal data as part of their search queries, among
which some might reveal sensitive information (e.g. health issues, sexual,
political or religious preferences). Several solutions exist to allow users
querying search engines while improving privacy protection. However, these
solutions suffer from a number of limitations: some are subject to user
re-identification attacks, while others lack scalability or are unable to
provide accurate results. This paper presents CYCLOSA, a secure, scalable and
accurate private Web search solution. CYCLOSA improves security by relying on
trusted execution environments (TEEs) as provided by Intel SGX. Further,
CYCLOSA proposes a novel adaptive privacy protection solution that reduces the
risk of user re- identification. CYCLOSA sends fake queries to the search
engine and dynamically adapts their count according to the sensitivity of the
user query. In addition, CYCLOSA meets scalability as it is fully
decentralized, spreading the load for distributing fake queries among other
nodes. Finally, CYCLOSA achieves accuracy of Web search as it handles the real
query and the fake queries separately, in contrast to other existing solutions
that mix fake and real query results
Rethinking Location Privacy for Unknown Mobility Behaviors
Location Privacy-Preserving Mechanisms (LPPMs) in the literature largely
consider that users' data available for training wholly characterizes their
mobility patterns. Thus, they hardwire this information in their designs and
evaluate their privacy properties with these same data. In this paper, we aim
to understand the impact of this decision on the level of privacy these LPPMs
may offer in real life when the users' mobility data may be different from the
data used in the design phase. Our results show that, in many cases, training
data does not capture users' behavior accurately and, thus, the level of
privacy provided by the LPPM is often overestimated. To address this gap
between theory and practice, we propose to use blank-slate models for LPPM
design. Contrary to the hardwired approach, that assumes known users' behavior,
blank-slate models learn the users' behavior from the queries to the service
provider. We leverage this blank-slate approach to develop a new family of
LPPMs, that we call Profile Estimation-Based LPPMs. Using real data, we
empirically show that our proposal outperforms optimal state-of-the-art
mechanisms designed on sporadic hardwired models. On non-sporadic location
privacy scenarios, our method is only better if the usage of the location
privacy service is not continuous. It is our hope that eliminating the need to
bootstrap the mechanisms with training data and ensuring that the mechanisms
are lightweight and easy to compute help fostering the integration of location
privacy protections in deployed systems
Constructing elastic distinguishability metrics for location privacy
With the increasing popularity of hand-held devices, location-based
applications and services have access to accurate and real-time location
information, raising serious privacy concerns for their users. The recently
introduced notion of geo-indistinguishability tries to address this problem by
adapting the well-known concept of differential privacy to the area of
location-based systems. Although geo-indistinguishability presents various
appealing aspects, it has the problem of treating space in a uniform way,
imposing the addition of the same amount of noise everywhere on the map. In
this paper we propose a novel elastic distinguishability metric that warps the
geometrical distance, capturing the different degrees of density of each area.
As a consequence, the obtained mechanism adapts the level of noise while
achieving the same degree of privacy everywhere. We also show how such an
elastic metric can easily incorporate the concept of a "geographic fence" that
is commonly employed to protect the highly recurrent locations of a user, such
as his home or work. We perform an extensive evaluation of our technique by
building an elastic metric for Paris' wide metropolitan area, using semantic
information from the OpenStreetMap database. We compare the resulting mechanism
against the Planar Laplace mechanism satisfying standard
geo-indistinguishability, using two real-world datasets from the Gowalla and
Brightkite location-based social networks. The results show that the elastic
mechanism adapts well to the semantics of each area, adjusting the noise as we
move outside the city center, hence offering better overall privacy
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A review paper on preserving privacy in mobile environments
Technology is improving day-by-day and so is the usage of mobile devices. Every activity that would involve manual and paper transactions can now be completed in seconds using your fingertips. On one hand, life has become fairly convenient with the help of mobile devices, whereas on the other hand security of the data and the transactions occurring in the process have been under continuous threat. This paper, re-evaluates the different policies and procedures used for preserving the privacy of sensitive data and device location.. Policy languages have been very vital in the mobile environments as they can be extended/used significantly for sending/receiving any data. In the mobile environment users always go to service providers to access various services. Hence, communications between the service providers and mobile handsets needs to be secured. Also, the data access control needs to be in place. A section of this paper will review the communication paths and channels and their related access criteria. This paper is a contribution to the mobile domain, showing the possible attacks related to privacy and the various mechanisms used to preserve the end-user privacy. In addition, it also gives acomparison of the different privacy preserving methods in mobile environments to provide guidance to the readers. Finally, the paper summarises future research challenges in the area of privacy preservation. This paper examines the ‘where’ problem and in particular, examines tradeoffs between enforcing location security at a device vs. enforcing location security at an edge location server. This paper also sketches an implementation of location security solution at both the device and the edge location server and presents detailed experiments using real mobility and user profile data sets collected from multiple data sources (taxicabs, Smartphones)
User-centric privacy preservation in Internet of Things Networks
Recent trends show how the Internet of Things (IoT) and its services are becoming more omnipresent and popular. The end-to-end IoT services that are extensively used include everything from neighborhood discovery to smart home security systems, wearable health monitors, and connected appliances and vehicles. IoT leverages different kinds of networks like Location-based social networks, Mobile edge systems, Digital Twin Networks, and many more to realize these services. Many of these services rely on a constant feed of user information. Depending on the network being used, how this data is processed can vary significantly. The key thing to note is that so much data is collected, and users have little to no control over how extensively their data is used and what information is being used. This causes many privacy concerns, especially for a na ̈ıve user who does not know the implications and consequences of severe privacy breaches. When designing privacy policies, we need to understand the different user data types used in these networks. This includes user profile information, information from their queries used to get services (communication privacy), and location information which is much needed in many on-the-go services. Based on the context of the application, and the service being provided, the user data at risk and the risks themselves vary. First, we dive deep into the networks and understand the different aspects of privacy for user data and the issues faced in each such aspect. We then propose different privacy policies for these networks and focus on two main aspects of designing privacy mechanisms: The quality of service the user expects and the private information from the user’s perspective. The novel contribution here is to focus on what the user thinks and needs instead of fixating on designing privacy policies that only satisfy the third-party applications’ requirement of quality of service
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Location Security - Where to Enforce?
Enforcing security in location based services is very crucial in the current mobile world. Past literature has examined both location and identity obfuscation techniques in order to optimally tradeoff security/privacy with utility − this primarily addresses the ‘how to enforce location security problem’; however, it does not address the ‘where to enforce location security problem’. This paper examines the ‘where’ problem and in particular, examines tradeoffs between enforcing location security at a device vs. enforcing location security at an edge location server. This paper also sketches an implementation of location security solutions at both the device and the edge location server and presents detailed experiments using real mobility and user profile data sets collected from various data sources (taxicabs, Smartphones). Our results show that while device-based solutions do not require trust in the edge location server, they either suffer from high false positive rate (about 25% probability of not meeting the desired security requirement) or low utility (about 600 meters higher error in obfuscated location data)
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