CONF Vitek_IJCB_2025/IDIAP Privacy-enhancing Sclera Segmentation Benchmarking Competition: SSBC 2025 Vitek, Matej Tomašević, Darian Das, Abhijit Nathan, Sabari Özbulak, Gökhan Tataroğlu Özbulak, Gözde Ayşe Calbimonte, Jean-Paul Anjos, André Hemant Bhatt, Hariohm Dhirendra Premani, Dhruv Chaudhari, Jay Wang, Caiyong Jiang, Jian Zhang, Chi Zhang, Qi Iyappan Ganapathi, Iyyakutti Sadaf Ali, Syed Velayudan, Divya Assefa, Maregu Werghi, Naoufel A Daniels, Zachary John, Leeon Vyas, Ritesh Nourmohammadi Khiarak, Jalil Akbari Saeed, Taher Nasehi, Mahsa Kianfar, Ali Pashazadeh Panahi, Mobina Sharma, Geetanjali Raj Panth, Pushp Ramachandra, Raghavendra Nigam, Aditya Pal, Umapada Pedrini, Helio Struc, Vitomir International Joint Conference on Biometrics 2025 IEEE This paper presents a summary of the 2025 Sclera Segmentation Benchmarking Competition (SSBC), which focused on the development of privacy-preserving sclera-segmentation models trained using synthetically generated ocular images. The goal of the competition was to evaluate how well models trained on synthetic data perform in comparison to those trained on real-world datasets. The competition featured two tracks: one relying solely on synthetic data for model development, and one combining/mixing synthetic with (a limited amount of) real-world data. A total of nine research groups submitted diverse segmentation models, employing a variety of architectural designs, including transformer-based solutions, lightweight models, and segmentation networks guided by generative frameworks. Experiments were conducted across three evaluation datasets containing both synthetic and real-world images, collected under diverse conditions. Results show that models trained entirely on synthetic data can achieve competitive performance, particularly when dedicated training strategies are employed, as evidenced by the top performing models that achieved scores of over in the synthetic data track. Moreover, performance gains in the mixed track were often driven more by methodological choices rather than by the inclusion of real data, highlighting the promise of synthetic data for privacy-aware biometric development. The code and data for the competition is available at: https://github.com/dariant/SSBC_2025.