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Research #1

CCV: Collaborative Validation of Public-Key Certificates for IoT by Distributed Caching

Public-key certificate validation is an important building block for various security protocols for IoT devices. However, certification validation incurs non-trivial overhead on resourceconstrained IoT devices, because it either requires long latency or large cache space. This work proposes a Collaborative Certificate Validation (CCV) protocol to utilize the power of distributed caching and explore the feasibility of using the cache spaces on all IoT devices as a large pool to store validated certificates. Evaluation results show that CCV only uses less than 25% validation time and reduces >90% decryption operations on each device, compared to a recent method.

Research #2

Vacuum Filters: More Space-Efficient and Faster Replacement for Bloom and Cuckoo Filters

Vacuum Filters: We present vacuum filters, a type of data structures to support approximate membership queries. Vacuum filters cost the smallest space among all known AMQ data structures and provide higher insertion and lookup throughput in most situations. Hence they can be used as the replacement of the widely used Bloom filters and cuckoo filters. Similar to cuckoo filters, vacuum filters also store item fingerprints in a table. The memory-efficiency and throughput improvements are from the innovation of a table insertion and fingerprint eviction strategy that achieves both high load factors and data locality without any restriction of the table size. The experiments show that vacuum filters can achieves 25% less space in average and similar throughput compared to cuckoo filters, and 15% less space and >10x throughput compared to Bloom filters, with same false positive rates.
In addition, we propose a new update framework Instant updates and periodical reconstructions (IUPR) to resolve two difficult problems for AMQ structures under dynamics, namely duplicate insertions and set resizing. The design of IUPR solves the performance downgrading problem caused by duplicate insertions and set resizing. This problem frequently happens in practical applications but has not been well-addressed by existing methods. IUPR is an extra component to further strengthen vacuum filters that work on practical memory hierarchies.