11 citations · 23 across the 9 of their papers we have counts for
10 papers
DynSL: Resource-Adaptive Model Training across Heterogeneous Edge Devices
Boris Radovič, Istemi Ekin Akkus, Marco Canini +4
Training models over data distributed across edge devices presents a trade-off: centralizing data simplifies training but raises privacy and regulatory concerns, while full local t…
Defeating Cerberus: Concept-Guided Privacy-Leakage Mitigation in Multimodal Language Models
Boyang Zhang, Istemi Ekin Akkus, Ruichuan Chen +4
Multimodal large language models (MLLMs) have demonstrated remarkable capabilities in processing and reasoning over diverse modalities, but their advanced abilities also raise sign…
Protecting Confidentiality, Privacy and Integrity in Collaborative Learning
Dong Chen, Alice Dethise, Istemi Ekin Akkus +6
A collaboration between dataset owners and model owners is needed to facilitate effective machine learning (ML) training. During this collaboration, however, dataset owners and mod…
Model Hijacking Attack in Federated Learning
Zheng Li, Siyuan Wu, Ruichuan Chen +6
Machine learning (ML), driven by prominent paradigms such as centralized and federated learning, has made significant progress in various critical applications ranging from autonom…
SMLT: A Serverless Framework for Scalable and Adaptive Machine Learning Design and Training
Ahsan Ali, Syed Zawad, Paarijaat Aditya +3
In today's production machine learning (ML) systems, models are continuously trained, improved, and deployed. ML design and training are becoming a continuous workflow of various t…
Citadel: Protecting Data Privacy and Model Confidentiality for Collaborative Learning with SGX
Chengliang Zhang, Junzhe Xia, Baichen Yang +6
With the advancement of machine learning (ML) and its growing awareness, many organizations who own data but not ML expertise (data owner) would like to pool their data and collabo…
Cybercasing 2.0: You Get What You Pay For
Jaeyoung Choi, Istemi Ekin Akkus, Serge Egelman +4
Under U.S. law, marketing databases exist under almost no legal restrictions concerning accuracy, access, or confidentiality. We explore the possible (mis)use of these databases in…
Approximate Distributed Joins in Apache Spark
Do Le Quoc, Istemi Ekin Akkus, Pramod Bhatotia +4
The join operation is a fundamental building block of parallel data processing. Unfortunately, it is very resource-intensive to compute an equi-join across massive datasets. The ap…
Sieve: Actionable Insights from Monitored Metrics in Microservices
Jörg Thalheim, Antonio Rodrigues, Istemi Ekin Akkus +5
Major cloud computing operators provide powerful monitoring tools to understand the current (and prior) state of the distributed systems deployed in their infrastructure. While suc…
The Case for a General and Interaction-based Third-party Cookie Policy
Istemi Ekin Akkus, Nicholas Weaver
The privacy implications of third-party tracking is a well-studied problem. Recent research has shown that besides data aggregators and behavioral advertisers, online social networ…