PINE: Efficient Norm-Bound Verification for Secret-Shared Vectors

• URL: http://arxiv.org/abs/2311.10237v2
• Date: Wed, 29 May 2024 16:47:17 GMT
• Title: PINE: Efficient Norm-Bound Verification for Secret-Shared Vectors
• Authors: Guy N. Rothblum, Eran Omri, Junye Chen, Kunal Talwar,
• Abstract summary: A two-server system such as PRIO allows for scalable aggregation of secret-shared vectors. Existing protocols for ensuring bounded-norm contributions either incur a large communication overhead, or only allow for approximate verification of the norm bound. We propose Private Inexpensive Norm Enforcement (PINE), a new protocol that allows exact norm verification with little communication overhead.
• Score: 25.30406294459483
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Secure aggregation of high-dimensional vectors is a fundamental primitive in federated statistics and learning. A two-server system such as PRIO allows for scalable aggregation of secret-shared vectors. Adversarial clients might try to manipulate the aggregate, so it is important to ensure that each (secret-shared) contribution is well-formed. In this work, we focus on the important and well-studied goal of ensuring that each contribution vector has bounded Euclidean norm. Existing protocols for ensuring bounded-norm contributions either incur a large communication overhead, or only allow for approximate verification of the norm bound. We propose Private Inexpensive Norm Enforcement (PINE): a new protocol that allows exact norm verification with little communication overhead. For high-dimensional vectors, our approach has a communication overhead of a few percent, compared to the 16-32x overhead of previous approaches.

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