Towards Differential Relational Privacy and its use in Question Answering

• URL: http://arxiv.org/abs/2203.16701v1
• Date: Wed, 30 Mar 2022 22:59:24 GMT
• Title: Towards Differential Relational Privacy and its use in Question Answering
• Authors: Simone Bombari, Alessandro Achille, Zijian Wang, Yu-Xiang Wang, Yusheng Xie, Kunwar Yashraj Singh, Srikar Appalaraju, Vijay Mahadevan, Stefano Soatto
• Abstract summary: Memorization of relation between entities in a dataset can lead to privacy issues when using a trained question answering model. We quantify this phenomenon and provide a possible definition of Differential Privacy (DPRP) We illustrate concepts in experiments with largescale models for Question Answering.
• Score: 109.4452196071872
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Memorization of the relation between entities in a dataset can lead to privacy issues when using a trained model for question answering. We introduce Relational Memorization (RM) to understand, quantify and control this phenomenon. While bounding general memorization can have detrimental effects on the performance of a trained model, bounding RM does not prevent effective learning. The difference is most pronounced when the data distribution is long-tailed, with many queries having only few training examples: Impeding general memorization prevents effective learning, while impeding only relational memorization still allows learning general properties of the underlying concepts. We formalize the notion of Relational Privacy (RP) and, inspired by Differential Privacy (DP), we provide a possible definition of Differential Relational Privacy (DrP). These notions can be used to describe and compute bounds on the amount of RM in a trained model. We illustrate Relational Privacy concepts in experiments with large-scale models for Question Answering.

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