American democracy is critically threatened by the use of insecure voting systems. Many existing electronic voting machines have malfunctioned during recent elections, and many are also vulnerable to hacking (Appel et al. 2019, Blaze et al. 2019, Blaze 2020). Most states have switched to secure, hand-marked paper ballots, but roughly 30% of Americans will continue to vote using vulnerable voting machines in 2020 (Cordova et al. 2019, Bajak 2020). While Congress allocated $380 million in 2018 and $425 million in 2020 to improve election security, these funds were neither targeted at nor sufficient for replacing all electronic voting machines. We propose that Congress (1) allocate $110 million exclusively for transitioning away from electronic voting machines and (2) prohibit the use of federal funds for purchasing voting systems that do not primarily use hand-marked paper ballots. This one-time transition cost is much smaller than even annual expenditures on other critical infrastructure (Copeland 2010; Halderman 2019). Replacing electronic voting machines with hand-marked paper ballots is the most affordable and secure option.

Restricting data plane processing to packet headers precludes analysis of payloads to improve routing and security decisions. Deep-Match delivers line-rate regular expression matching on payloads using Network Processors (NPs). It further supports packet reordering to match patterns in flows that cross packet boundaries. Our evaluation shows that an implementation of DeepMatch, on a 40 Gbps Netronome NFP-6000 SmartNIC, achieves up to line rate for streams of unrelated packets and up to 20 Gbps when searches span multiple packets within a flow. In contrast with prior work, this throughput is data-independent and adds no burstiness. DeepMatch opens new opportunities for programmable data planes.