International Association
for Cryptologic Research

We construct an efficient pseudorandom correlation generator (PCG) (Boyle et al., Crypto'19) for two-party {\it programmable oblivious linear evaluation (OLE)} functionality over $\mathbb{F}_2$. Our construction (i) has an efficient seed expansion phase, and (ii) comes with a concretely efficient protocol for distributing the seeds that makes black-box use of cryptography and runs in a constant number of rounds. PCGs for programmable OLE are known to imply PCGs for generating $n$-party Beaver triples over $\mathbb{F}_2$. The resultant PCG has a seed setup phase whose communication cost is $n(n-1)$ times than that of the programmable OLE protocol. The per-party seed size and the seed expansion time have a multiplicative overhead of $2(n-1)$. Prior constructions for efficiently generating multiparty Beaver triples only worked for finite fields $\mathbb{F}_q$ where $q \geq 3$ or required one bit of per-party communication for each triple generated (and hence, do not satisfy the PCG definition). Thus, ours is the first concretely efficient PCG for generating Beaver triples over $\mathbb{F}_2$ in the multiparty setting. Our distributed seed generation protocol generates $N = 2^{30}$ two-party programmable OLEs in 3.5 minutes with 255 MB of communication over a LAN network. The PCG seed size is around 55 MB and the expansion phase requires 10 PRG calls and around 229 thousand XOR and AND operations per triple, producing roughly 31,000 triples per second. Our PCG for generating multiparty Beaver triples has lower concrete communication cost than the state-of-the-art for small number of parties. When compared to the FOLEAGE protocol (Bombar et al, Asiacrypt 2024) which requires one bit of per-party communication per triple that is generated, our communication cost is lower by $2.4\times$ when generating $N = 2^{36}$ triples between three parties and is $1.2 \times $ lower for the case of five parties. At a conceptual level, our protocol deviates from the prior approaches which relied on variants of dual learning parity with noise (LPN) assumption. Instead, our construction combines both the primal and dual versions of LPN to achieve the aforementioned efficiency.

Private set intersection (PSI) allows two mutually distrusting parties each holding a private set of elements, to learn the intersection of their sets without revealing anything beyond the intersection. Recent work (Badrinarayanan et al., PoPETS'22) initiates the study of updatable PSI (UPSI), which allows the two parties to compute PSI on a regular basis with sets that constantly get updated, where both the computation and communication complexity only grow with the size of the small updates and not the large entire sets. However, there are several limitations of their presented protocols. First, they can only be used to compute the plain PSI functionality and do not support extended functionalities such as PSI-Cardinality and PSI-Sum. Second, they only allow parties to add new elements to their existing set and do not support arbitrary deletion of elements. Finally, their addition-only protocols either require both parties to learn the output or only achieve low complexity in an amortized sense and incur linear worst-case complexity. In this work, we address all the above limitations. In particular, we study UPSI with semi-honest security in both the addition-only and addition-deletion settings. We present new protocols for both settings that support plain PSI as well as extended functionalities including PSI-Cardinality and PSI-Sum, achieving one-sided output (which implies two-sided output). In the addition-only setting, we also present a protocol for a more general functionality Circuit-PSI that outputs secret shares of the intersection. All of our protocols have worst-case computation and communication complexity that only grow with the set updates instead of the entire sets (except for a polylogarithmic factor). We implement our new UPSI protocols and compare with the state-of-the-art protocols for PSI and extended functionalities. Our protocols compare favorably when the total set sizes are sufficiently large, the new updates are sufficiently small, or in networks with low bandwidth.