Scaling Private Set Intersection to Billion-Element Sets

Seny Kamara, Payman Mohassel, Mariana Raykova, and Saeed Sadeghian


We examine the feasibility of private set intersection (PSI) over massive

datasets. PSI, which allows two parties to find the intersection of their sets

without revealing them to each other, has numerous applications including to

privacy-preserving data mining, location-based services and genomic

computations. Unfortunately, the most efficient constructions only scale to

sets containing a few thousand elements---even in the semi-honest model and

over a LAN.

In this work, we design PSI protocols in the server-aided setting, where the

parties have access to a single untrusted server that makes its

computational resources available as a service. We show that by exploiting the

server-aided model and by carefully optimizing and parallelizing our

implementations, PSI is feasible for billion-element sets even while

communicating over the Internet. As far as we know, ours is the first

attempt to scale PSI to billion-element sets which represents an increase of

five orders of magnitude over previous work.

Our protocols are secure in several adversarial models including against a

semi-honest, covert and malicious server; and address a range of security and

privacy concerns including fairness and the leakage of the intersection size.

Our protocols also yield efficient server-aided private equality-testing (PET)

with stronger security guarantees than prior work.


Publication typeTechReport
> Publications > Scaling Private Set Intersection to Billion-Element Sets