Journal of Applied Language Learning(JALL)

Ph. D. of Tel Aviv University, The Lester and Sally Antin Faculty of Humanities, School of Philosophy, Linguistics and Science Studies, Israel

Publications

• Research Article   
  Applying Deep Personal Privacy (DPP) An Empirical Framework for Inference Resistance in Large Language Models
  Author(s): Yair Oppenheim*
  
  This paper introduces an empirical extension of the Deep Personal Privacy (DPP) framework, a novel paradigm that reconcep- tualizes privacy as resistance to inference rather than mere control over data disclosure. Unlike traditional privacy-preserving approaches-such as k-anonymity, l-diversity, t-closeness, and differential privacy-which primarily focus on data access and identifiability, the DPP framework models privacy as impedance within an inference network. The core contribution of this work lies in operationalizing DPP within embedding-based systems, particularly large language models (LLMs), where sensitive information can be inferred through semantic alignment rather than explicit disclosure. We establish a formal mathematical relationship between cosine similarity, inference probability, and privacy impedance, demon- strating that reducing semantic alignment system.. Read More»
  

  Abstract HTML PDF