Ioannis Demertzis (University of Maryland), Javad Ghareh Chamani (Hong Kong University of Science and Technology & Sharif University of Technology), Dimitrios Papadopoulos (Hong Kong University of Science and Technology), Charalampos Papamanthou (University of Maryland)

We study the problem of dynamic searchable encryption (DSE) with forward-and-backward privacy. Many DSE schemes have been proposed recently but the most efficient ones have one limitation: they require maintaining an operation counter for each unique keyword, either stored locally at the client or accessed obliviously (e.g., with an oblivious map) at the server, during every operation. We propose three new schemes that overcome the above limitation and achieve constant permanent client storage with improved performance, both asymptotically and experimentally, compared to prior state-of-the-art works. In particular, our first two schemes adopt a "static-to-dynamic" transformation which eliminates the need for oblivious accesses during searches. Due to this, they are the first practical schemes with minimal client storage and non-interactive search. Our third scheme is the first quasi-optimal forward-and-backward DSE scheme with only a logarithmic overhead for retrieving the query result (independently of previous deletions). While it does require an oblivious access during search in order to keep permanent client storage minimal, its practical performance is up to four orders of magnitude better than the best existing scheme with quasi-optimal search.

View More Papers

BLAZE: Blazing Fast Privacy-Preserving Machine Learning

Arpita Patra (Indian Institute of Science, Bangalore), Ajith Suresh (Indian Institute of Science, Bangalore)

Read More

Deceptive Previews: A Study of the Link Preview Trustworthiness...

Giada Stivala (CISPA Helmholtz Center for Information Security), Giancarlo Pellegrino (CISPA Helmholtz Center for Information Security)

Read More

HotFuzz: Discovering Algorithmic Denial-of-Service Vulnerabilities Through Guided Micro-Fuzzing

William Blair (Boston University), Andrea Mambretti (Northeastern University), Sajjad Arshad (Northeastern University), Michael Weissbacher (Northeastern University), William Robertson (Northeastern University), Engin Kirda (Northeastern University), Manuel Egele (Boston University)

Read More

CloudLeak: Large-Scale Deep Learning Models Stealing Through Adversarial Examples

Honggang Yu (University of Florida), Kaichen Yang (University of Florida), Teng Zhang (University of Central Florida), Yun-Yun Tsai (National Tsing Hua University), Tsung-Yi Ho (National Tsing Hua University), Yier Jin (University of Florida)

Read More