78 citations · 146 across the 6 of their papers we have counts for
6 papers
Membership Inference Attacks Against Time-Series Models
Noam Koren, Abigail Goldsteen, Guy Amit +1
Analyzing time-series data that contains personal information, particularly in the medical field, presents serious privacy concerns. Sensitive health data from patients is often us…
SoK: Reducing the Vulnerability of Fine-tuned Language Models to Membership Inference Attacks
Guy Amit, Abigail Goldsteen, Ariel Farkash
Natural language processing models have experienced a significant upsurge in recent years, with numerous applications being built upon them. Many of these applications require fine…
HeLayers: A Tile Tensors Framework for Large Neural Networks on Encrypted Data
Ehud Aharoni, Allon Adir, Moran Baruch +9
Privacy-preserving solutions enable companies to offload confidential data to third-party services while fulfilling their government regulations. To accomplish this, they leverage…
Data Minimization for GDPR Compliance in Machine Learning Models
Abigail Goldsteen, Gilad Ezov, Ron Shmelkin +2
The EU General Data Protection Regulation (GDPR) mandates the principle of data minimization, which requires that only data necessary to fulfill a certain purpose be collected. How…
Anonymizing Machine Learning Models
Abigail Goldsteen, Gilad Ezov, Ron Shmelkin +2
There is a known tension between the need to analyze personal data to drive business and privacy concerns. Many data protection regulations, including the EU General Data Protectio…
Reducing Risk of Model Inversion Using Privacy-Guided Training
Abigail Goldsteen, Gilad Ezov, Ariel Farkash
Machine learning models often pose a threat to the privacy of individuals whose data is part of the training set. Several recent attacks have been able to infer sensitive informati…