Pith Number

pith:DRHO3H4Q

pith:2026:DRHO3H4QRUFHF3FRAYAQZERM5L

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Privacy Preserving Multi Agent Path Finding

Guy Shani, Roni Stern, Rotem Lev Lehman

Adding mock agents during planning lets multiple agents find collision-free paths without revealing their exact planned locations to each other.

arxiv:2605.14119 v1 · 2026-05-13 · cs.MA

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Claims

C1strongest claim

We propose a general framework for obtaining planning-level privacy, which works by adding mock agents to the planning process. We show how to adapt two popular MAPF algorithms, namely PIBT and LaCAM, such that they preserve execution-level privacy. Lastly, we propose a post-processing technique that allows the agents to reduce the sum of costs of the returned solution without losing any privacy.

C2weakest assumption

The assumption that inserting mock agents during planning sufficiently obscures real agent locations for all participants without introducing detectable information leaks or violating collision constraints, and that the privacy-preserving modifications to PIBT and LaCAM retain the original algorithms' correctness properties.

C3one line summary

New algorithms for multi-agent path finding preserve planning-level privacy via mock agents and execution-level privacy via modified PIBT and LaCAM solvers, plus post-processing that lowers total cost without breaking privacy.

References

32 extracted · 32 resolved · 0 Pith anchors

[1] Shahar Bardugo, Daniel Koyfman, and Dor Atzmon. 2025. Finding All Optimal So- lutions in Multi-Agent Path Finding. InInternational Symposium on Combinatorial Search. 20–28 2025

[2] Ronen I Brafman. 2015. A privacy preserving algorithm for multi-agent planning and search. In24th International Joint Conference on Artificial Intelligence, IJCAI 2015

[3] International Joint Conferences on Artificial Intelligence, 1530–1536

[4] Stepan Dergachev and Konstantin Yakovlev. 2021. Distributed multi-agent naviga- tion based on reciprocal collision avoidance and locally confined multi-agent path finding. InIEEE International Confere 2021

[5] Boi Faltings, Thomas Léauté, and Adrian Petcu. 2008. Privacy guarantees through distributed constraint satisfaction. In2008 IEEE/WIC/ACM International Confer- ence on Web Intelligence and Intelligent 2008

Receipt and verification

First computed	2026-05-17T23:39:11.915804Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

1c4eed9f908d0a72ecb106010c922ceae269bd21b23af4794a44e956389c755e

Aliases

arxiv: 2605.14119 · arxiv_version: 2605.14119v1 · doi: 10.48550/arxiv.2605.14119 · pith_short_12: DRHO3H4QRUFH · pith_short_16: DRHO3H4QRUFHF3FR · pith_short_8: DRHO3H4Q

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/DRHO3H4QRUFHF3FRAYAQZERM5L \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 1c4eed9f908d0a72ecb106010c922ceae269bd21b23af4794a44e956389c755e

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "77f4ac55f97f8b7225bed20155c0a606f7d0b261f440ddb627acd2b44c5cc545",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.MA",
    "submitted_at": "2026-05-13T21:08:24Z",
    "title_canon_sha256": "7f8db822a2321c74fd69d381343361097a1e6a14b0b0be7cd4acf24080680085"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.14119",
    "kind": "arxiv",
    "version": 1
  }
}