Welcome to the resource topic for
**2022/735**

**Title:**

Multiparty Private Set Intersection Cardinality and Its Applications

**Authors:**
Ni Trieu, Avishay Yanai, and Jiahui Gao

**Abstract:**

We describe a new paradigm for multi-party private set intersection cardinality (PSI-CA) that allows n parties to compute the intersection size of their datasets without revealing any additional information. We explore a variety of instantiations of this paradigm. Our protocols avoid computationally expensive public-key operations and are secure in the presence of a malicious adversary. We demonstrate the practicality of our PSI-CA protocol with an implementation. For n = 16 parties with data-sets of 2^20 items each, our server-aided variant takes 71 seconds. Interestingly, in the server-less setting, the same task takes only 7 seconds. To the best of our knowledge, this is the first ‘special purpose’ implementation of a multi-party PSI-CA (i.e., an implementation that does not rely on a generic underlying MPC protocol). Our PSI-CA protocols can be used to securely compute the dot-product function. The dot-product function takes n binary vectors v1, …, vn, each of m elements, and outputs the sum of m entries, where the i-th entry is equal the product of the i-th entries in all n input vectors. Importantly, the complexity of our protocol for secure dot-product (where party Pi has a secret vector vi) is linear only in the Hamming weight of the vectors, which is potentially sub-linear in the input size. We demonstrate that two interesting applications, namely, ‘COVID-19 heatmap’ and ‘associated rule learning (ARL)’, can be computed securely using a dot-product as a building block. We analyse the performance of securely computing Covid-19 heatmap and ARL using our protocol and compare that to the state-of-the-art.

**ePrint:**
https://eprint.iacr.org/2022/735

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