Randomization Techniques to Mitigate the Risk of Copyright Infringement

• URL: http://arxiv.org/abs/2408.13278v1
• Date: Wed, 21 Aug 2024 20:55:00 GMT
• Title: Randomization Techniques to Mitigate the Risk of Copyright Infringement
• Authors: Wei-Ning Chen, Peter Kairouz, Sewoong Oh, Zheng Xu,
• Abstract summary: We investigate potential randomization approaches that can complement current practices for copyright protection. This is motivated by the inherent ambiguity of the rules that determine substantial similarity in copyright precedents. Similar randomized approaches, such as differential privacy, have been successful in mitigating privacy risks.
• Score: 48.75580082851766
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we investigate potential randomization approaches that can complement current practices of input-based methods (such as licensing data and prompt filtering) and output-based methods (such as recitation checker, license checker, and model-based similarity score) for copyright protection. This is motivated by the inherent ambiguity of the rules that determine substantial similarity in copyright precedents. Given that there is no quantifiable measure of substantial similarity that is agreed upon, complementary approaches can potentially further decrease liability. Similar randomized approaches, such as differential privacy, have been successful in mitigating privacy risks. This document focuses on the technical and research perspective on mitigating copyright violation and hence is not confidential. After investigating potential solutions and running numerical experiments, we concluded that using the notion of Near Access-Freeness (NAF) to measure the degree of substantial similarity is challenging, and the standard approach of training a Differentially Private (DP) model costs significantly when used to ensure NAF. Alternative approaches, such as retrieval models, might provide a more controllable scheme for mitigating substantial similarity.

Related papers

• Probabilistic Analysis of Copyright Disputes and Generative AI Safety [0.0]
  The paper provides a structured analysis of key evidentiary principles, with particular emphasis on the "inverse ratio rule" The paper examines the heightened copyright risks posed by generative AI, highlighting how extensive access to copyrighted material by generative models increases the risk of infringement. The analysis reveals that while the Near Access-Free (NAF) condition mitigates some infringement risks, its justifiability and efficacy are questionable in certain contexts.
  arXiv  Detail & Related papers  (2024-10-01T08:05:19Z)
• Sequential Manipulation Against Rank Aggregation: Theory and Algorithm [119.57122943187086]
  We leverage an online attack on the vulnerable data collection process. From the game-theoretic perspective, the confrontation scenario is formulated as a distributionally robust game. The proposed method manipulates the results of rank aggregation methods in a sequential manner.
  arXiv  Detail & Related papers  (2024-07-02T03:31:21Z)
• Sample-efficient neural likelihood-free Bayesian inference of implicit HMMs [1.8843687952462742]
  We propose a novel, sample-efficient likelihood-free method for estimating the high-dimensional hidden states of an implicit HMM. Our approach relies on learning directly the intractable posterior distribution of the hidden states, using an autoregressive-flow, by exploiting the Markov property.
  arXiv  Detail & Related papers  (2024-05-02T21:13:34Z)
• Uncertainty-guided Open-Set Source-Free Unsupervised Domain Adaptation with Target-private Class Segregation [22.474866164542302]
  UDA approaches commonly assume that source and target domains share the same labels space. This paper considers the more challenging Source-Free Open-set Domain Adaptation (SF-OSDA) setting. We propose a novel approach for SF-OSDA that exploits the granularity of target-private categories by segregating their samples into multiple unknown classes.
  arXiv  Detail & Related papers  (2024-04-16T13:52:00Z)
• Model-Based Epistemic Variance of Values for Risk-Aware Policy Optimization [59.758009422067]
  We consider the problem of quantifying uncertainty over expected cumulative rewards in model-based reinforcement learning. We propose a new uncertainty Bellman equation (UBE) whose solution converges to the true posterior variance over values. We introduce a general-purpose policy optimization algorithm, Q-Uncertainty Soft Actor-Critic (QU-SAC) that can be applied for either risk-seeking or risk-averse policy optimization.
  arXiv  Detail & Related papers  (2023-12-07T15:55:58Z)
• Simulation-based, Finite-sample Inference for Privatized Data [14.218697973204065]
  We propose a simulation-based "repro sample" approach to produce statistically valid confidence intervals and hypothesis tests. We show that this methodology is applicable to a wide variety of private inference problems.
  arXiv  Detail & Related papers  (2023-03-09T15:19:31Z)
• Derandomized Novelty Detection with FDR Control via Conformal E-values [20.864605211132663]
  We propose to make conformal inferences more stable by leveraging suitable conformal e-values instead of p-values. We show that the proposed method can reduce randomness without much loss of power compared to standard conformal inference.
  arXiv  Detail & Related papers  (2023-02-14T19:21:44Z)
• Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyond [57.10914865054868]
  We consider vertical logistic regression (VLR) trained with mini-batch descent gradient. We provide a comprehensive and rigorous privacy analysis of VLR in a class of open-source Federated Learning frameworks.
  arXiv  Detail & Related papers  (2022-07-19T05:47:30Z)
• Risk Consistent Multi-Class Learning from Label Proportions [64.0125322353281]
  This study addresses a multiclass learning from label proportions (MCLLP) setting in which training instances are provided in bags. Most existing MCLLP methods impose bag-wise constraints on the prediction of instances or assign them pseudo-labels. A risk-consistent method is proposed for instance classification using the empirical risk minimization framework.
  arXiv  Detail & Related papers  (2022-03-24T03:49:04Z)
• Trust but Verify: Assigning Prediction Credibility by Counterfactual Constrained Learning [123.3472310767721]
  Prediction credibility measures are fundamental in statistics and machine learning. These measures should account for the wide variety of models used in practice. The framework developed in this work expresses the credibility as a risk-fit trade-off.
  arXiv  Detail & Related papers  (2020-11-24T19:52:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.