AGeneralized Binary Tree Mechanism for Private Approximation of All-Pair Shortest Distances
–Neural Information Processing Systems
We study the problem of approximating all-pair distances in a weighted undirected graph with differential privacy, introduced by Sealfon [Sea16]. Given a publicly known undirected graph, we treat the weights of edges as sensitive information, and two graphs are neighbors if their edge weights differ in one edge by at most one. We obtain efficient algorithms with significantly improved bounds on a broad class of graphs which we refer to as recursively separable. In particular, for any n-vertex Kh-minor-free graph, our algorithm achieve an additive error of eO(h(nW)1/3), where W represents the maximum edge weight; For grid graphs, the same algorithmic scheme achieve additive error of eO(n1/4 W). Our approach can be seen as a generalization of the celebrated binary tree mechanism for range queries, as releasing range queries is equivalent to computing all-pair distances on a path graph. In essence, our approach is based on generalizing the binary tree mechanism to graphs that are recursively separable. JL and ZZ have been supported by National Science Foundation of China under Grant No. 62472212 and the New Cornerstone Science Foundation. Supported in part by NSF award 2228995 JU's research was funded by the NSFCNS 2433628, Google Seed Fund grant, Google Research Scholar Award, Dean Research Seed Fund, and Rutgers Decanal Grant no.
Neural Information Processing Systems
Jun-23-2026, 06:27:31 GMT
- Country:
- Europe (1.00)
- Asia (1.00)
- North America > United States (0.87)
- Genre:
- Research Report > Experimental Study (1.00)
- Industry:
- Information Technology > Security & Privacy (1.00)
- Technology:
- Information Technology
- Security & Privacy (1.00)
- Information Management (1.00)
- Data Science (0.92)
- Artificial Intelligence
- Machine Learning (1.00)
- Natural Language (0.68)
- Information Technology