Demo Data from EDBT 2026 / reviewers in the wild / expert
Pascal Fischli
dblp:216/5581
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Privacy and data protection › privacy-preserving computation
encrypted data processing |
0.3 | 1 | 2017 | Secure Sharing of Partially Homomorphic Encrypted IoT Data · SenSys 2017 |
Privacy and data protection › privacy-preserving data sharing
encrypted data sharing |
0.3 | 1 | 2017 | Secure Sharing of Partially Homomorphic Encrypted IoT Data · SenSys 2017 |
Cryptographic primitives and cryptanalysis › homomorphic encryption
partially homomorphic encryption |
0.3 | 1 | 2017 | Secure Sharing of Partially Homomorphic Encrypted IoT Data · SenSys 2017 |
Internet of things and sensor networks › iot security
iot data security |
0.1 | 1 | 2017 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | Secure Sharing of Partially Homomorphic Encrypted IoT DataabstractIoT 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 |
SenSys | 4 |