## CryptoDB

### Jaspal Singh

#### Publications

**Year**

**Venue**

**Title**

2023

CRYPTO

Malicious Secure, Structure-Aware Private Set Intersection
Abstract

Structure-Aware PSI (saPSI) is a variant of PSI where Alice's input set $A$ has some publicly known structure and Bob's input $B$ is an unstructured set of points and Alice wants to learn the intersection $A \cap B$. It was recently introduced by Garimella et al. (Crypto 2022); they present a semi-honest saPSI protocol with communication that scales with the description size of Alice's set, instead of its cardinality. In this paper, we present the first saPSI protocol secure against malicious-adversaries.
We use a cut-and-choose approach to ensure that Alice uses valid FSS sharings, of the same underlying object. In order to handle a technical issue that arises, we introduce a new variant of function secret sharing, called derandomizable FSS (dFSS).
We show how to extend prior FSS constructions for union of geometric balls, to meet the requirements of dFSS. Additionally, we improve FSS constructions that result in asymptotic improvements to the prior semi-honest structure-aware PSI protocol.

2022

CRYPTO

Structure-Aware Private Set Intersection, With Applications to Fuzzy Matching
📺
Abstract

In two-party private set intersection (PSI), Alice holds a set $X$, Bob holds a set $Y$, and they learn (only) the contents of $X \cap Y$.
We introduce \textbf{structure-aware PSI} protocols, which take advantage of situations where Alice's set $X$ is publicly known to have a certain structure.
The goal of structure-aware PSI is to have communication that scales with the \emph{description size} of Alice's set, rather its \emph{cardinality}.
We introduce a new generic paradigm for structure-aware PSI based on function secret-sharing (FSS).
In short, if there exists compact FSS for a class of structured sets, then there exists a semi-honest PSI protocol that supports this class of input sets, with communication cost proportional only to the FSS share size.
Several prior protocols for efficient (plain) PSI can be viewed as special cases of our new paradigm, with an implicit FSS for unstructured sets.
Our PSI protocol can be instantiated from a significantly weaker flavor of FSS, which has not been previously studied.
We develop several improved FSS techniques that take advantage of these relaxed requirements, and which are in some cases exponentially better than existing FSS.
Finally, we explore in depth a natural application of structure-aware PSI.
If Alice's set $X$ is the union of many radius-$\delta$ balls in some metric space, then an intersection between $X$ and $Y$ corresponds to \textbf{fuzzy PSI}, in which the parties learn which of their points are within distance $\delta$.
In structure-aware PSI, the communication cost scales with the number of balls in Alice's set, rather than their total volume.
Our techniques lead to efficient fuzzy PSI for $\ell_\infty$ and $\ell_1$ metrics (and approximations of $\ell_2$ metric) in high dimensions.
We implemented this fuzzy PSI protocol for 2-dimensional $\ell_\infty$ metrics.
For reasonable input sizes, our protocol requires 45--60\% less time and 85\% less communication than competing approaches that simply reduce the problem to plain PSI.

2021

PKC

Private Set Operations from Oblivious Switching
📺
Abstract

Private set intersection reveals the intersection of two private sets, but many real-world applications require the parties to learn $\textit{only}$ partial information} about the intersection.
In this paper, we introduce a new approach for computing arbitrary functions of the intersection, provided that it is safe to also reveal the cardinality of the intersection. In the most general case, our new protocol provides the participants with secret shares of the intersection, which can be fed into any generic 2PC protocol. Certain computations on the intersection can also be done even more directly and efficiently, avoiding this secret-sharing step. These cases include computing $\textit{only}$ the cardinality of the intersection, or the ``cardinality-sum'' application proposed in Ion $\textit{et al.}$ (ePrint 2017). Compared to the state-of-the-art protocol for computing on the intersection (Pinkas et al., Eurocrypt 2019), our protocol has about $2.5-3\times$ less communication and has faster running time on slower (50Mbps) networks.
Our new techniques can also be used to privately compute the {\em union} of two sets as easily as computing the intersection. Our protocol concretely improves the leading private set union protocol (Kolesnikov et al., Asiacrypt 2020) by a factor of $2-2.5\times$, depending on the network speed. We then show how private set union can be used in a simple way to realize the ``Private-ID'' functionality suggested by Buddhavarapu et al.~(ePrint 2020). Our protocol is significantly faster than the prior Private-ID protocol, especially on fast networks.
All of our protocols are in the two-party setting and are secure against semi-honest adversaries.

2021

CRYPTO

Large Message Homomorphic Secret Sharing from DCR and Applications
📺
Abstract

We present the first homomorphic secret sharing (HSS) construction that simultaneously (1) has negligible correctness error, (2) supports integers from an exponentially large range, and (3) relies on an assumption not known to imply FHE --- specifically, the Decisional Composite Residuosity (DCR) assumption. This resolves an open question posed by Boyle, Gilboa, and Ishai (Crypto 2016). Homomorphic secret sharing is analogous to fully-homomorphic encryption, except the ciphertexts are shared across two non-colluding evaluators. Previous constructions of HSS either had non-negligible correctness error and polynomial-size plaintext space or were based on the stronger LWE assumption. We also present two applications of our technique: a multi-server ORAM with constant bandwidth overhead, and a rate-$1$ trapdoor hash function with negligible error rate.

#### Coauthors

- Gayathri Garimella (3)
- Payman Mohassel (1)
- Mike Rosulek (3)
- Lawrence Roy (1)
- Saeed Sadeghian (1)