Zobrazeno 1 - 10
of 744
pro vyhledávání: '"Pyrgelis A"'
In this paper, we address the problem of privacy-preserving hyperparameter (HP) tuning for cross-silo federated learning (FL). We first perform a comprehensive measurement study that benchmarks various HP strategies suitable for FL. Our benchmarks sh
Externí odkaz:
http://arxiv.org/abs/2402.16087
Lawful Interception (LI) is a legal obligation of Communication Service Providers (CSPs) to provide interception capabilities to Law Enforcement Agencies (LEAs) in order to gain insightful data from network communications for criminal proceedings, e.
Externí odkaz:
http://arxiv.org/abs/2308.14164
Autor:
Intoci, Francesco, Sav, Sinem, Pyrgelis, Apostolos, Bossuat, Jean-Philippe, Troncoso-Pastoriza, Juan Ramon, Hubaux, Jean-Pierre
Homomorphic encryption (HE), which allows computations on encrypted data, is an enabling technology for confidential cloud computing. One notable example is privacy-preserving Prediction-as-a-Service (PaaS), where machine-learning predictions are com
Externí odkaz:
http://arxiv.org/abs/2305.00690
Autor:
Froelicher, David, Cho, Hyunghoon, Edupalli, Manaswitha, Sousa, Joao Sa, Bossuat, Jean-Philippe, Pyrgelis, Apostolos, Troncoso-Pastoriza, Juan R., Berger, Bonnie, Hubaux, Jean-Pierre
Principal component analysis (PCA) is an essential algorithm for dimensionality reduction in many data science domains. We address the problem of performing a federated PCA on private data distributed among multiple data providers while ensuring data
Externí odkaz:
http://arxiv.org/abs/2304.00129
Autor:
Chatel, Sylvain, Knabenhans, Christian, Pyrgelis, Apostolos, Troncoso, Carmela, Hubaux, Jean-Pierre
Homomorphic encryption, which enables the execution of arithmetic operations directly on ciphertexts, is a promising solution for protecting privacy of cloud-delegated computations on sensitive data. However, the correctness of the computation result
Externí odkaz:
http://arxiv.org/abs/2207.14071
Autor:
Sav, Sinem, Diaa, Abdulrahman, Pyrgelis, Apostolos, Bossuat, Jean-Philippe, Hubaux, Jean-Pierre
We present RHODE, a novel system that enables privacy-preserving training of and prediction on Recurrent Neural Networks (RNNs) in a cross-silo federated learning setting by relying on multiparty homomorphic encryption. RHODE preserves the confidenti
Externí odkaz:
http://arxiv.org/abs/2207.13947
Publikováno v:
Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 5, 2, Article 54 (June 2021)
Wearable devices such as smartwatches, fitness trackers, and blood-pressure monitors process, store, and communicate sensitive and personal information related to the health, life-style, habits and interests of the wearer. This data is exchanged with
Externí odkaz:
http://arxiv.org/abs/2105.11172
Publikováno v:
Proceedings on Privacy Enhancing Technologies (PoPETs), Vol. 2021, Issue 3
Tree-based models are among the most efficient machine learning techniques for data mining nowadays due to their accuracy, interpretability, and simplicity. The recent orthogonal needs for more data and privacy protection call for collaborative priva
Externí odkaz:
http://arxiv.org/abs/2103.08987
Autor:
Halimi, Anisa, Dervishi, Leonard, Ayday, Erman, Pyrgelis, Apostolos, Troncoso-Pastoriza, Juan Ramon, Hubaux, Jean-Pierre, Jiang, Xiaoqian, Vaidya, Jaideep
Providing provenance in scientific workflows is essential for reproducibility and auditability purposes. Workflow systems model and record provenance describing the steps performed to obtain the final results of a computation. In this work, we propos
Externí odkaz:
http://arxiv.org/abs/2101.08879
Autor:
Efstratios-Stylianos Pyrgelis, George P. Paraskevas, Vasilios C. Constantinides, Fotini Boufidou, Leonidas Stefanis, Elisabeth Kapaki
Publikováno v:
Biomedicines, Vol 12, Iss 8, p 1898 (2024)
Idiopathic normal-pressure hydrocephalus (iNPH) is a clinic-radiological neurological syndrome presenting with cognitive deficits, gait disturbances and urinary incontinence. It often coexists with Alzheimer’s disease (AD). Due to the reversible na
Externí odkaz:
https://doaj.org/article/5161dfa98fe243a08c9eeee4ce034131
