Hamid Mozaffari (University of Massachusetts Amherst), Amir Houmansadr (University of Massachusetts Amherst)

Private information retrieval (PIR) enables clients to query and retrieve data from untrusted servers without the untrusted servers learning which data was retrieved.

In this paper, we present a new class of multi-server PIR protocols, which we call emph{heterogeneous PIR (HPIR)}. In such multi-server PIR protocols, the computation and communication overheads imposed on the PIR servers are non-uniform, i.e., some servers handle higher computation/communication burdens than the others. This enables heterogeneous PIR protocols to be suitable for a range of new PIR applications.

What enables us to enforce such heterogeneity is a unique PIR-tailored secret sharing algorithm that we leverage in building our PIR protocol.

We have implemented our HPIR protocol and evaluated its performance in comparison with regular PIR protocols. Our evaluations demonstrate that a querying client can trade off the computation and communication loads of the (heterogeneous) PIR servers by adjusting some parameters. For example in a two server scenario with a heterogeneity degree of $4/1$, to retrieve a $456$KB file from a $0.2$GB database, the rich (i.e., resourceful) PIR server will do $1.1$ seconds worth of computation compared to $0.3$ seconds by the poor (resource-constrained) PIR server; this is while each of the servers would do the same $1$ seconds of computation in a homogeneous settings. Also, for this given example, our HPIR protocol will impose $912$KB communication bandwidth on the rich server compared to $228$KB on the poor server (by contrast to $456$KB overhead on each of the servers for a traditional homogeneous design).

View More Papers

PhantomCache: Obfuscating Cache Conflicts with Localized Randomization

Qinhan Tan (Zhejiang University), Zhihua Zeng (Zhejiang University), Kai Bu (Zhejiang University), Kui Ren (Zhejiang University)

Read More

BLAG: Improving the Accuracy of Blacklists

Sivaramakrishnan Ramanathan (University of Southern California/Information Sciences Institute), Jelena Mirkovic (University of Southern California/Information Sciences Institute), Minlan Yu (Harvard University)

Read More

Decentralized Control: A Case Study of Russia

Reethika Ramesh (University of Michigan), Ram Sundara Raman (University of Michgan), Matthew Bernhard (University of Michigan), Victor Ongkowijaya (University of Michigan), Leonid Evdokimov (Independent), Anne Edmundson (Independent), Steven Sprecher (University of Michigan), Muhammad Ikram (Macquarie University), Roya Ensafi (University of Michigan)

Read More

On the Resilience of Biometric Authentication Systems against Random...

Benjamin Zi Hao Zhao (University of New South Wales and Data61 CSIRO), Hassan Jameel Asghar (Macquarie University and Data61 CSIRO), Mohamed Ali Kaafar (Macquarie University and Data61 CSIRO)

Read More