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Ryan Henry

Abstract

Private information retrieval (PIR) is a way for clients to query a remote database without the database holder learning the clients’ query terms or the responses they generate. Compelling applications for PIR are abound in the cryptographic and privacy research literature, yet existing PIR techniques are notoriously inefficient. Consequently, no such PIRbased application to date has seen real-world at-scale deployment. This paper proposes new “batch coding” techniques to help address PIR’s efficiency problem. The new techniques exploit the connection between ramp secret sharing schemes and efficient information-theoretically secure PIR (IT-PIR) protocols. This connection was previously observed by Henry, Huang, and Goldberg (NDSS 2013), who used ramp schemes to construct efficient “batch queries” with which clients can fetch several database records for the same cost as fetching a single record using a standard, non-batch query. The new techniques in this paper generalize and extend those of Henry et al. to construct “batch codes” with which clients can fetch several records for only a fraction the cost of fetching a single record using a standard non-batch query over an unencoded database. The batch codes are highly tuneable, providing a means to trade off (i) lower server-side computation cost, (ii) lower server-side storage cost, and/or (iii) lower uni- or bi-directional communication cost, in exchange for a comparatively modest decrease in resilience to Byzantine database servers.

Open access

A Bit More Than a Bit Is More Than a Bit Better

Faster (essentially) optimal-rate many-server PIR

Syed Mahbub Hafiz and Ryan Henry

Abstract

We study both the practical and theoretical efficiency of private information retrieval (PIR) protocols in a model wherein several untrusted servers work to obliviously service remote clients’ requests for data and yet no pair of servers colludes in a bid to violate said obliviousness. In exchange for such a strong security assumption, we obtain new PIR protocols exhibiting remarkable efficiency with respect to every cost metric—download, upload, computation, and round complexity—typically considered in the PIR literature.

The new constructions extend a multiserver PIR protocol of Shah, Rashmi, and Ramchandran (ISIT 2014), which exhibits a remarkable property of its own: to fetch a b-bit record from a collection of r such records, the client need only download b + 1 bits total. We find that allowing “a bit more” download (and optionally introducing computational assumptions) yields a family of protocols offering very attractive trade-offs. In addition to Shah et al.’s protocol, this family includes as special cases (2-server instances of) the seminal protocol of Chor, Goldreich, Kushilevitz, and Sudan (FOCS 1995) and the recent DPF-based protocol of Boyle, Gilboa, and Ishai (CCS 2016). An implicit “folklore” axiom that dogmatically permeates the research literature on multiserver PIR posits that the latter protocols are the “most efficient” protocols possible in the perfectly and computationally private settings, respectively. Yet our findings soundly refute this supposed axiom: These special cases are (by far) the least performant representatives of our family, with essentially all other parameter settings yielding instances that are significantly faster.

Open access

Bradley Keith Fritz, Wesley Clint Hoffmann, Zbigniew Czaczyk, William Bagley, Greg Kruger and Ryan Henry

Abstract

An increasing number of spray nozzle and agrochemical manufacturers are incorporating droplet size measurements into both research and development. Each laboratory invariably has their own sampling setup and procedures. This is particularly true about measurement distance from the nozzle and concurrent airflow velocities. Both have been shown to significantly impact results from laser diffraction instruments. These differences can be overcome through the use of standardized reference nozzles and relative spray classification categories. Sets of references nozzles, which defined a set of classification category thresholds, were evaluated for droplet size under three concurrent air flow velocities (0.7, 3.1 and 6.7 m/s). There were significant, though numerically small, differences in the droplet size data between identical reference nozzles. The resulting droplet size data were used to categorize a number of additional spray nozzles at multiple pressure and air flow velocities. This was done to determine if similar classifications were given across the different airspeeds. Generally, droplet size classifications agreed for all airspeeds, with the few that did not, only differing by one category. When reporting droplet size data, it is critical that data generated from a set of reference nozzles also be presented as a means of providing a relative frame of reference.