Name
Title | ||
|---|---|---|
Differential Privacy from Locally Adjustable Graph Algorithms: k-Core Decomposition, Low Out-Degree Ordering, and Densest Subgraphs |
Abstract | ||
|---|---|---|
Differentially private algorithms allow large-scale data analytics while preserving user privacy. Designing such algorithms for graph data is gaining importance with the growth of large networks that model various (sensitive) relationships between individuals. While there exists a rich history of important literature in this space, to the best of our knowledge, no results formalize a relationship between certain parallel and distributed graph algorithms and differentially private graph analysis. In this paper, we define locally adjustable graph algorithms and show that algorithms of this type can be transformed into differentially private algorithms. Our formalization is motivated by a set of results that we present in the central and local models of differential privacy for a number of problems, including k-core decomposition, low out-degree ordering, and densest subgraphs. First, we design an $\varepsilon$-edge differentially private (DP) algorithm that returns a subset of nodes that induce a subgraph of density at least $ \frac{D^{*}}{1+\eta}-O(\operatorname{poly}(\log n)/\varepsilon)$, where $D^{*}$ is the density of the densest subgraph in the input graph (for any constant $\eta\gt 0$). This algorithm achieves a two-fold improvement on the multiplicative approximation factor of the previously best-known private densest subgraph algorithms while maintaining a near-linear runtime. Then, we present an $\varepsilon$-locally edge differentially private (LEDP) algorithm for k-core decompositions. Our LEDP algorithm provides approximates the core numbers (for any constant $\eta\gt 0$) with $(2+\eta)$ multiplicative and $O(\operatorname{poly}(\log n)/\varepsilon)$ additive error. This is the first differentially private algorithm that outputs private k-core decomposition statistics. We also modify our algorithm to return a differentially private low out-degree ordering of the nodes, where orienting the edges from nodes earlier in the ordering to nodes later in the ordering results in out-degree at most $O(d+$ poly $(\log n)/\varepsilon$) (where d is the degeneracy of the graph). A small modification to the algorithm also yields a $\varepsilon$-LEDP algorithm for $(4+\eta,O(\operatorname{poly}(\log n)/\varepsilon))$ approximate densest subgraph (which returns both the set of nodes in the subgraph and its density). Our algorithm uses $O(\log^{2}n)$ rounds of communication between the curator and individual nodes. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/FOCS54457.2022.00077 | 2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS) |
Keywords | DocType | ISSN |
differentially private graph algorithms,densest subgraph,k-core decomposition,low-out-degree-ordering,local-differentially-private-graph-algorithms | Conference | 1523-8288 |
ISBN | Citations | PageRank |
978-1-6654-5520-6 | 0 | 0.34 |
References | Authors | |
49 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Laxman Dhulipala | 1 | 78 | 6.51 |
| Quanquan Liu | 2 | 7 | 7.90 |
| Sofya Raskhodnikova | 3 | 991 | 64.59 |
| Jessica Shi | 4 | 2 | 1.72 |
| Julian Shun | 5 | 593 | 32.57 |
| Shangdi Yu | 6 | 0 | 1.01 |