Privacy Preserving Summation

Privacy-preserving summation focuses on securely computing the sum of data held by multiple parties without revealing individual contributions. Current research emphasizes developing efficient and robust algorithms, such as adaptations of sketching techniques and secure multi-party computation protocols, often incorporating differential privacy to further enhance confidentiality. A key challenge lies in mitigating reconstruction attacks, where an adversary attempts to infer individual data points from multiple summation results, with recent work exploring topological properties of the data network to improve resilience. These advancements are crucial for enabling collaborative data analysis in sensitive domains like federated learning while safeguarding individual privacy.