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Katarzyna Olejnik

dblp:201/9235 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2017
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
1 paper
Privacy and data protection · 70% Authentication and access control · 30%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Privacy and data protection
mobile privacy
0.312017
SmarPer: Context-Aware and Automatic Runtime-Permissions for Mobile Devices · IEEE Symposium on Security and Privacy 2017
Authentication and access control › access control › permission management
permission systems
0.312017
SmarPer: Context-Aware and Automatic Runtime-Permissions for Mobile Devices · IEEE Symposium on Security and Privacy 2017
Privacy and data protection › mobile privacy
runtime permission management
0.312017
SmarPer: Context-Aware and Automatic Runtime-Permissions for Mobile Devices · IEEE Symposium on Security and Privacy 2017
Privacy and data protection › privacy preferences
privacy preference modeling
0.112017
SmarPer: Context-Aware and Automatic Runtime-Permissions for Mobile Devices · IEEE Symposium on Security and Privacy 2017

Methods — techniques the papers use, named apart from their topics

obfuscation · 0.3machine learning · 0.3bayesian linear regression · 0.3
YearPublicationVenuePosition
2017 SmarPer: Context-Aware and Automatic Runtime-Permissions for Mobile Devices
abstract
Permission systems are the main defense that mobile platforms, such as Android and iOS, offer to users to protect their private data from prying apps. However, due to the tension between usability and control, such systems have several limitations that often force users to overshare sensitive data. We address some of these limitations with SmarPer, an advanced permission mechanism for Android. To address the rigidity of current permission systems and their poor matching of users' privacy preferences, SmarPer relies on contextual information and machine learning methods to predict permission decisions at runtime. Note that the goal of SmarPer is to mimic the users' decisions, not to make privacy-preserving decisions per se. Using our SmarPer implementation, we collected 8,521 runtime permission decisions from 41 participants in real conditions. With this unique data set, we show that using an efficient Bayesian linear regression model results in a mean correct classification rate of 80% (±3%). This represents a mean relative reduction of approximately 50% in the number of incorrect decisions when compared with a user-defined static permission policy, i.e., the model used in current permission systems. SmarPer also focuses on the suboptimal trade-off between privacy and utility, instead of only "allow" or "deny" type of decisions, SmarPer also offers an "obfuscate" option where users can still obtain utility by revealing partial information to apps. We implemented obfuscation techniques in SmarPer for different data types and evaluated them during our data collection campaign. Our results show that 73% of the participants found obfuscation useful and it accounted for almost a third of the total number of decisions. In short, we are the first to show, using a large dataset of real in situ permission decisions, that it is possible to learn users' unique decision patterns at runtime using contextual information while supporting data obfuscation, this is an important step towards automating the management of permissions in smartphones.
Katarzyna Olejnik, Italo Dacosta, Joana Soares Machado, Kévin Huguenin, Mohammad Emtiyaz Khan, Jean-Pierre Hubaux
IEEE Symposium on Security and Privacy1