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Machine Learning and Knowledge Discovery in Databases. Research Track and Demo Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024. 

As the metaverse grows with the advances of new technologies, a number of researchers have raised the concern on the privacy of motion data in virtual reality (VR). It is becoming clear that motion data can reveal essential information of people, such as user identification. However, the fundamental problems about what types of motion data, how to process, and on what ranges of VR applications are still underexplored. This work summarizes the work of motion data privacy on these aspects from both the fields of VR and data privacy. Our results demonstrate that researchers from both fields have recognized the importance of the problem, while there are differences due to the focused problems. A variety of VR studies have been used for user identification, and the results are affected by the application types and ranges of involved actions. We also review the biometrics work from related fields including the behaviors of keystrokes and waist as well as data of skeleton, face and fingerprint. At the end, we discuss our findings and suggest future work to protect the privacy of motion data. 

Preserving differential privacy has been well studied under the centralized setting. However, it’s very challenging to preserve differential privacy under multiparty setting, especially for the vertically partitioned case. In this work, we propose a new framework for differential privacy preserving multiparty learning in the vertically partitioned setting. Our core idea is based on the functional mechanism that achieves differential privacy of the released model by adding noise to the objective function. We show the server can simply dissect the objective function into single-party and cross-party sub-functions, and allocate computation and perturbation of their polynomial coefficients t o l ocal p arties. Our method n eeds o nly o ne r ound of noise addition and secure aggregation. The released model in our framework achieves the same utility as applying the functional mechanism in the centralized setting. Evaluation on real-world and synthetic datasets for linear and logistic regressions shows the effectiveness of our proposed method.