# Which PPML Would a User Choose? A Structured Decision Support Framework for Developers to Rank PPML Techniques Based on User Acceptance Criteria

arXiv: https://arxiv.org/abs/2411.06995 · HTML: https://mlsanity.com/paper/2411.06995/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 81 references that are in the map.
- First posted on arXiv 11 Nov 2024.

## Builds on (most cited first, 14 of 14)

- [Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning](https://mlsanity.com/paper/1712.05526/) (2017)
- [FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks](https://mlsanity.com/paper/2104.08815/) (2021)
- [Privacy-Preserving Machine Learning: Methods, Challenges and Directions](https://mlsanity.com/paper/2108.04417/) (2021)
- [Federated Learning Meets Natural Language Processing: A Survey](https://mlsanity.com/paper/2107.12603/) (2021)
- [A Differentially Private Text Perturbation Method Using a Regularized Mahalanobis Metric](https://mlsanity.com/paper/2010.11947/) (2020)
- [Benchmarking Differential Privacy and Federated Learning for BERT Models](https://mlsanity.com/paper/2106.13973/) (2021)
- [Privacy enabled Financial Text Classification using Differential Privacy and Federated Learning](https://mlsanity.com/paper/2110.01643/) (2021)
- [Differential Privacy in Natural Language Processing: The Story So Far](https://mlsanity.com/paper/2208.08140/) (2022)
- [Privacy-Preserving Text Classification on BERT Embeddings with Homomorphic Encryption](https://mlsanity.com/paper/2210.02574/) (2022)
- [A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunities](https://mlsanity.com/paper/2210.08906/) (2022)
- [When Differential Privacy Meets Interpretability: A Case Study](https://mlsanity.com/paper/2106.13203/) (2021)
- [Evaluation of Federated Learning in Phishing Email Detection](https://mlsanity.com/paper/2007.13300/) (2020)
- [SafeNet: The Unreasonable Effectiveness of Ensembles in Private Collaborative Learning](https://mlsanity.com/paper/2205.09986/) (2022)
- [MPC-Pipe: an Efficient Pipeline Scheme for Secure Multi-party Machine Learning Inference](https://mlsanity.com/paper/2209.13643/) (2022)

## Abstract (arXiv)

Using Privacy-Enhancing Technologies (PETs) for machine learning often influences the characteristics of a machine learning approach, e.g., the needed computational power, timing of the answers or how the data can be utilized. When designing a new service, the developer faces the problem that some decisions require a trade-off. For example, the use of a PET may cause a delay in the responses or adding noise to the data to improve the users' privacy might have a negative impact on the accuracy of the machine learning approach. As of now, there is no structured way how the users' perception of a machine learning based service can contribute to the selection of Privacy Preserving Machine Learning (PPML) methods. This is especially a challenge since one cannot assume that users have a deep technical understanding of these technologies. Therefore, they can only be asked about certain attributes that they can perceive when using the service and not directly which PPML they prefer. This study introduces a decision support framework with the aim of supporting the selection of PPML technologies based on user preferences. Based on prior work analysing User Acceptance Criteria (UAC), we translate these criteria into differentiating characteristics for various PPML techniques. As a final result, we achieve a technology ranking based on the User Acceptance Criteria while providing technology insights for the developers. We demonstrate its application using the use case of classifying privacy-relevant information. Our contribution consists of the decision support framework which consists of a process to connect PPML technologies with UAC, a process for evaluating the characteristics that separate PPML techniques, and a ranking method to evaluate the best PPML technique for the use case.