activity
20152026
most citedPersonalized Security Indicators to Detect Application Phishing Attacks in Mobile Platforms

28 citations · 30 across the 7 of their papers we have counts for

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Showing cs.CRShow all

10 papers · 1 filter

cs.CR2026

On Securing the Software Development Lifecycle in IoT RISC-V Trusted Execution Environments

Annika Wilde, Samira Briongos, Claudio Soriente +1

RISC-V-based Trusted Execution Environments (TEEs) are gaining traction in the automotive and IoT sectors as a foundation for protecting sensitive computations. However, the suppor…

cs.CR2026

On Abnormal Execution Timing of Conditional Jump Instructions

Annika Wilde, Samira Briongos, Claudio Soriente +1

An extensive line of work on modern computing architectures has shown that the execution time of instructions can (i) depend on the operand of the instruction or (ii) be influenced…

cs.CR2026

The Real Menace of Cloning Attacks on SGX Applications

Annika Wilde, Samira Briongos, Claudio Soriente +1

Trusted Execution Environments (TEEs) are gaining popularity as an effective means to provide confidentiality in the cloud. TEEs, such as Intel SGX, suffer from so-called rollback…

cs.CR2024

The Forking Way: When TEEs Meet Consensus

Annika Wilde, Tim Niklas Gruel, Claudio Soriente +1

An increasing number of distributed platforms combine Trusted Execution Environments (TEEs) with blockchains. Indeed, many hail the combination of TEEs and blockchains a good "marr…

cs.CR2023

No Forking Way: Detecting Cloning Attacks on Intel SGX Applications

Samira Briongos, Ghassan Karame, Claudio Soriente +1

Forking attacks against TEEs like Intel SGX can be carried out either by rolling back the application to a previous state, or by cloning the application and by partitioning its inp…

cs.CR20232 cited

LISA: LIghtweight single-server Secure Aggregation with a public source of randomness

Elina van Kempen, Qifei Li, Giorgia Azzurra Marson +1

Secure Aggregation (SA) is a key component of privacy-friendly federated learning applications, where the server learns the sum of many user-supplied gradients, while individual gr…