Samuel Maddock

14 papers A* 3Journal 10Unranked 1

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	GEM+: Scalable State-of-the-Art Private Synthetic Data with Generator Networks. CoRR Samuel Maddock, Shripad Gade, Graham Cormode, Will Bullock
2025	J	`jnl`	Leveraging Vertical Public-Private Split for Improved Synthetic Data Generation. CoRR Samuel Maddock, Shripad Gade, Graham Cormode, Will Bullock
2025	J	`jnl`	Private Federated Multiclass Post-hoc Calibration. CoRR Samuel Maddock, Graham Cormode, Carsten Maple
2025	—	`conf`	Synthetic Tabular Data: Methods, Attacks and Defenses. KDD (2) Graham Cormode, Samuel Maddock, Enayat Ullah, Shripad Gade
2025	J	`jnl`	Synthetic Tabular Data: Methods, Attacks and Defenses. CoRR Graham Cormode, Samuel Maddock, Enayat Ullah, Shripad Gade
2025	J	`jnl`	Synthetic Tabular Data: methods, attacks and defenses. Proc. VLDB Endow. Graham Cormode, Shripad Gade, Samuel Maddock, Enayat Ullah
2024	A*	`conf`	FLAIM: AIM-based Synthetic Data Generation in the Federated Setting. KDD Samuel Maddock, Graham Cormode, Carsten Maple
2023	A*	`conf`	CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning. ICLR Samuel Maddock, Alexandre Sablayrolles, Pierre Stock
2023	J	`jnl`	FLAIM: AIM-based Synthetic Data Generation in the Federated Setting. CoRR Samuel Maddock, Graham Cormode, Carsten Maple
2022	J	`jnl`	CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning. CoRR Samuel Maddock, Alexandre Sablayrolles, Pierre Stock
2022	A*	`conf`	Federated Boosted Decision Trees with Differential Privacy. CCS Samuel Maddock, Graham Cormode, Tianhao Wang, Carsten Maple, Somesh Jha
2022	J	`jnl`	Federated Boosted Decision Trees with Differential Privacy. CoRR Samuel Maddock, Graham Cormode, Tianhao Wang, Carsten Maple, Somesh Jha
2021	J	`jnl`	Frequency Estimation under Local Differential Privacy [Experiments, Analysis and Benchmarks]. CoRR Graham Cormode, Samuel Maddock, Carsten Maple
2021	J	`jnl`	Frequency Estimation under Local Differential Privacy. Proc. VLDB Endow. Graham Cormode, Samuel Maddock, Carsten Maple

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.