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Bibliographic Details
Main Authors:	Krüger, Fabian P., Östman, Johan, Mervin, Lewis, Tetko, Igor V., Engkvist, Ola
Format:	Preprint
Published:	2024
Subjects:	Cryptography and Security Machine Learning
Online Access:	https://arxiv.org/abs/2410.16975
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Secure Aggregation is Not Private Against Membership Inference Attacks
by: Ngo, Khac-Hoang, et al.
Published: (2024)

Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity Models
by: Friesacher, Hannah Rosa, et al.
Published: (2024)

Provable Privacy Attacks on Trained Shallow Neural Networks
by: Smorodinsky, Guy, et al.
Published: (2024)

Privacy-Preserving 3-Layer Neural Network Training
by: Chiang, John
Published: (2023)

DP-Dueling: Learning from Preference Feedback without Compromising User Privacy
by: Saha, Aadirupa, et al.
Published: (2024)

FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation
by: Xhemrishi, Marvin, et al.
Published: (2023)

Practical Feasibility of Gradient Inversion Attacks in Federated Learning
by: Valadi, Viktor, et al.
Published: (2025)

Training Data Reconstruction: Privacy due to Uncertainty?
by: Runkel, Christina, et al.
Published: (2024)

Does Training with Synthetic Data Truly Protect Privacy?
by: Zhao, Yunpeng, et al.
Published: (2025)

Enhancing Uncertainty Quantification in Drug Discovery with Censored Regression Labels
by: Svensson, Emma, et al.
Published: (2024)

Modeling Neural Networks with Privacy Using Neural Stochastic Differential Equations
by: Hong, Sanghyun, et al.
Published: (2025)

Preserving Node-level Privacy in Graph Neural Networks
by: Xiang, Zihang, et al.
Published: (2023)

Privacy-Preserving Verifiable Neural Network Inference Service
by: Riasi, Arman, et al.
Published: (2024)

Functional Encryption in Secure Neural Network Training: Data Leakage and Practical Mitigations
by: Ioniţă, Alexandru, et al.
Published: (2025)

Detection of Compromised Functions in a Serverless Cloud Environment
by: Lavi, Danielle, et al.
Published: (2024)

Private and Robust Contribution Evaluation in Federated Learning
by: Velez, Delio Jaramillo, et al.
Published: (2026)

Fake or Compromised? Making Sense of Malicious Clients in Federated Learning
by: Mozaffari, Hamid, et al.
Published: (2024)

DPAR: Decoupled Graph Neural Networks with Node-Level Differential Privacy
by: Zhang, Qiuchen, et al.
Published: (2022)

Local Differential Privacy in Graph Neural Networks: a Reconstruction Approach
by: Bhaila, Karuna, et al.
Published: (2023)

Can Graph Neural Networks Expose Training Data Properties? An Efficient Risk Assessment Approach
by: Yuan, Hanyang, et al.
Published: (2024)

Detecting Compromised IoT Devices Using Autoencoders with Sequential Hypothesis Testing
by: Mainuddin, Md, et al.
Published: (2024)

Breaking Agents: Compromising Autonomous LLM Agents Through Malfunction Amplification
by: Zhang, Boyang, et al.
Published: (2024)

On the Evaluation of User Privacy in Deep Neural Networks using Timing Side Channel
by: Shukla, Shubhi, et al.
Published: (2022)

Stealing Training Graphs from Graph Neural Networks
by: Lin, Minhua, et al.
Published: (2024)

Generate-then-Verify: Reconstructing Data from Limited Published Statistics
by: Liu, Terrance, et al.
Published: (2025)

Degree-Preserving Randomized Response for Graph Neural Networks under Local Differential Privacy
by: Hidano, Seira, et al.
Published: (2022)

A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability
by: Dai, Enyan, et al.
Published: (2022)

PrivDiffuser: Privacy-Guided Diffusion Model for Data Obfuscation in Sensor Networks
by: Yang, Xin, et al.
Published: (2024)

Tempo: Confidentiality Preservation in Cloud-Based Neural Network Training
by: Xu, Rongwu, et al.
Published: (2024)

Byzantine-Resilient Secure Aggregation for Federated Learning Without Privacy Compromises
by: Xia, Yue, et al.
Published: (2024)

Compromising Embodied Agents with Contextual Backdoor Attacks
by: Liu, Aishan, et al.
Published: (2024)

Decoupling Generalizability and Membership Privacy Risks in Neural Networks
by: Fang, Xingli, et al.
Published: (2026)

Towards Deep Encrypted Training: Low-Latency, Memory-Efficient, and High-Throughput Inference for Privacy-Preserving Neural Networks
by: Njungle, Nges Brian, et al.
Published: (2026)

Indiscriminate Data Poisoning Attacks on Neural Networks
by: Lu, Yiwei, et al.
Published: (2022)

Erased but Not Forgotten: How Backdoors Compromise Concept Erasure
by: Braun, Tobias, et al.
Published: (2025)

Diverse Community Data for Benchmarking Data Privacy Algorithms
by: Sen, Aniruddha, et al.
Published: (2023)

Click Without Compromise: Online Advertising Measurement via Per User Differential Privacy
by: Xiao, Yingtai, et al.
Published: (2024)

Data Privacy Preservation on the Internet of Things
by: Sen, Jaydip, et al.
Published: (2023)

Managing Correlations in Data and Privacy Demand
by: Chaudhuri, Syomantak, et al.
Published: (2025)

if (filemtime($filename) + $maxlifetime < time()) { unlink($filename); $count++; } } return $count; } /** * A function that is called internally when session data is to be saved. * * @param string $sessId The current session ID * @param string $data The session data to write * * @return bool */ protected function saveSession($sessId, $data): bool { $sessFile = $this->path . '/sess_' . $sessId; if ($handle = fopen($sessFile, 'w')) { $return = false; // Lock the file for exclusive access to avoid issues with multiple // processes writing session simultaneously: if (flock($handle, LOCK_EX)) { $return = fwrite($handle, $data); // Make sure that there's no trailing data by truncating the file to // the correct length: ftruncate($handle, strlen($data)); fflush($handle); flock($handle, LOCK_UN); } fclose($handle); if ($return !== false) { return true; } } // If we got this far, something went wrong with the file output... // It is tempting to throw an exception here, but this code is called // outside of the context of exception handling, so all we can do is // echo a message.

// Anecdotal testing Today and Yesterday seems to indicate destroy() // is called by the garbage collector and everything is good. // Something to keep in mind though. return 0; } /** * Write function that is called when session data is to be saved. * * @param string $sessId The current session ID * @param string $data The session data to write * * @return bool */ public function write($sessId, $data): bool { if ($this->writesDisabled) { return true; } return $this->saveSession($sessId, $data); } /** * A function that is called internally when session data is to be saved. * * @param string $sessId The current session ID * @param string $data The session data to write * * @return bool */ abstract protected function saveSession($sessId, $data): bool; }

* * @return void */ public function writeClose() { // The assumption is that we're using PHP's ext/session. // session_write_close() will actually overwrite $_SESSION with an // empty array on completion -- which leads to a mismatch between what // is in the storage object and $_SESSION. To get around this, we // temporarily reset $_SESSION to an array, and then re-link it to // the storage object. // // Additionally, while you _can_ write to $_SESSION following a // session_write_close() operation, no changes made to it will be // flushed to the session handler. As such, we now mark the storage // object isImmutable. $storage = $this->getStorage(); if (! $storage->isImmutable()) { $_SESSION = $storage->toArray(true); session_write_close(); $storage->fromArray($_SESSION); $storage->markImmutable(); } } /** * Attempt to set the session name * * If the session has already been started, or if the name provided fails * validation, an exception will be raised. * * @param string $name * @return SessionManager * @throws Exception\InvalidArgumentException */ public function setName($name) { if ($this->sessionExists()) { throw new Exception\InvalidArgumentException( 'Cannot set session name after a session has already started'

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