Andrick Adhikari (University of Denver), Sanchari Das (University of Denver), Rinku Dewri (University of Denver)

The effectiveness of natural language privacy policies continues to be clouded by concerns surrounding their readability, ambiguity, and accessibility. Despite multiple design alternatives proposed over the years, natural language policies are still the primary format for organizations to communicate privacy practices to users. Current NLP techniques are often drawn towards generating high-level overviews, or specialized towards a single aspect of consumer privacy communication; the flexibility to apply them for multiple tasks is missing. To this aid, we present PolicyPulse, an information extraction pipeline designed to process privacy policies into usable formats. PolicyPulse employs a specialized XLNet classifier, and leverages a BERT-based model for semantic role labeling to extract phrases from policy sentences, while maintaining the semantic relations between predicates and their arguments. Our classification model was trained on 13,946 manually annotated semantic frames, and achieves a F1-score of 0.97 on identifying privacy practices communicated using clauses within a sentence. We emphasize the versatility of PolicyPulse through prototype applications to support requirement-driven policy presentations, question-answering systems, and privacy preference checking.

View More Papers

Kronos: A Secure and Generic Sharding Blockchain Consensus with...

Yizhong Liu (Beihang University), Andi Liu (Beihang University), Yuan Lu (Institute of Software Chinese Academy of Sciences), Zhuocheng Pan (Beihang University), Yinuo Li (Xi’an Jiaotong University), Jianwei Liu (Beihang University), Song Bian (Beihang University), Mauro Conti (University of Padua)

Read More

CENSOR: Defense Against Gradient Inversion via Orthogonal Subspace Bayesian...

Kaiyuan Zhang (Purdue University), Siyuan Cheng (Purdue University), Guangyu Shen (Purdue University), Bruno Ribeiro (Purdue University), Shengwei An (Purdue University), Pin-Yu Chen (IBM Research AI), Xiangyu Zhang (Purdue University), Ninghui Li (Purdue University)

Read More

DRAGON: Predicting Decompiled Variable Data Types with Learned Confidence...

Caleb Stewart, Rhonda Gaede, Jeffrey Kulick (University of Alabama in Huntsville)

Read More

ProvGuard: Detecting SDN Control Policy Manipulation via Contextual Semantics...

Ziwen Liu (Beihang University), Jian Mao (Beihang University; Tianmushan Laboratory; Hangzhou Innovation Institute, Beihang University), Jun Zeng (National University of Singapore), Jiawei Li (Beihang University; National University of Singapore), Qixiao Lin (Beihang University), Jiahao Liu (National University of Singapore), Jianwei Zhuge (Tsinghua University; Zhongguancun Laboratory), Zhenkai Liang (National University of Singapore)

Read More