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Pascal Fischli

dblp:216/5581 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · none

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

Computer networks · 1

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 · 67% Cryptographic primitives and cryptanalysis · 33%
Computer networks
1 paper
Internet of things and sensor networks · 100%

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

TopicWeightPapersLastEvidence papers
Privacy and data protection › privacy-preserving computation
encrypted data processing
0.312017
Secure Sharing of Partially Homomorphic Encrypted IoT Data · SenSys 2017
Privacy and data protection › privacy-preserving data sharing
encrypted data sharing
0.312017
Secure Sharing of Partially Homomorphic Encrypted IoT Data · SenSys 2017
Cryptographic primitives and cryptanalysis › homomorphic encryption
partially homomorphic encryption
0.312017
Secure Sharing of Partially Homomorphic Encrypted IoT Data · SenSys 2017
Internet of things and sensor networks › iot security
iot data security
0.112017
Secure Sharing of Partially Homomorphic Encrypted IoT Data · SenSys 2017

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

re-encryption · 0.6partially homomorphic encryption · 0.6key-update · 0.3key update · 0.3
YearPublicationVenuePosition
2017 Secure Sharing of Partially Homomorphic Encrypted IoT Data
abstract
IoT applications often utilize the cloud to store and provide ubiquitous access to collected data. This naturally facilitates data sharing with third-party services and other users, but bears privacy risks, due to data breaches or unauthorized trades with user data. To address these concerns, we present Pilatus, a data protection platform where the cloud stores only encrypted data, yet is still able to process certain queries (e.g., range, sum). More importantly, Pilatus features a novel encrypted data sharing scheme based on re-encryption, with revocation capabilities and in situ key-update. Our solution includes a suite of novel techniques that enable efficient partially homomorphic encryption, decryption, and sharing. We present performance optimizations that render these cryptographic tools practical for mobile platforms. We implement a prototype of Pilatus and evaluate it thoroughly. Our optimizations achieve a performance gain within one order of magnitude compared to state-of-the-art realizations; mobile devices can decrypt hundreds of data points in a few hundred milliseconds. Moreover, we discuss practical considerations through two example mobile applications (Fitbit and Ava) that run Pilatus on real-world data.
Hossein Shafagh, Anwar Hithnawi, Lukas Burkhalter, Pascal Fischli, Simon Duquennoy
SenSys4