Online voting is convenient and flexible, but amplifies the risks of voter coercion and vote buying. One promising mitigation strategy enables voters to give a coercer fake voting credentials, which silently cast votes that do not count. Current systems along these lines make problematic assumptions about credential issuance, however, such as strong trust in a registrar and/or in voter-controlled hardware, or expecting voters to interact with multiple registrars. Votegral is the first coercion-resistant voting architecture that leverages the physical security of in-person registration to address these credential-issuance challenges, amortizing the convenience costs of in-person registration by reusing credentials across successive elections. Votegral's registration component, TRIP, gives voters a kiosk in a privacy booth with which to print real and fake credentials on paper, eliminating dependence on trusted hardware in credential issuance. The voter learns and can verify in the privacy booth which credential is real, but real and fake credentials thereafter appear indistinguishable to others. Only voters actually under coercion, a hopefully-rare case, need to trust the kiosk. To achieve verifiability, each paper credential encodes an interactive zero-knowledge proof, which is sound in real credentials but unsound in fake credentials. Voters observe the difference in the order of printing steps, but need not understand the technical details. Experimental results with our prototype suggest that Votegral is practical and sufficiently scalable for real-world elections. User-visible latency of credential issuance in TRIP is at most 19.7 seconds even on resource-constrained kiosk hardware, making it suitable for registration at remote locations or on battery power. A companion usability study indicates that TRIP's usability is competitive with other e-voting systems including some lacking coercion resistance, and formal proofs support TRIP's combination of coercion-resistance and verifiability.

Online voting is attractive for convenience and accessibility, but is more susceptible to voter coercion and vote buying than in-person voting. One mitigation is to give voters fake voting credentials that they can yield to a coercer. Fake credentials appear identical to real ones, but cast votes that are silently omitted from the final tally. An important unanswered question is how ordinary voters perceive such a mitigation: whether they could understand and use fake credentials, and whether the coercion risks justify the costs of mitigation. We present the first systematic study of these questions, involving 150 diverse individuals in Boston, Massachusetts. All participants "registered" and "voted" in a mock election: 120 were exposed to coercion resistance via fake credentials, the rest forming a control group. Of the 120 participants exposed to fake credentials, 96% understood their use. 53% reported that they would create fake credentials in a real-world voting scenario, given the opportunity. 10% mistakenly voted with a fake credential, however. 22% reported either personal experience with or direct knowledge of coercion or vote-buying incidents. These latter participants rated the coercion-resistant system essentially as trustworthy as in-person voting via hand-marked paper ballots. Of the 150 total participants to use the system, 87% successfully created their credentials without assistance; 83% both successfully created and properly used their credentials. Participants give a System Usability Scale score of 70.4, which is slightly above the industry’s average score of 68. Our findings appear to support the importance of the coercion problem in general, and the promise of fake credentials as a possible mitigation, but user error rates remain an important usability challenge for future work.

Front-running attacks, which benefit from advanced knowledge of pending transactions, have proliferated in the blockchain space since the emergence of decentralized finance. Front-running causes devastating losses to honest participants and continues to endanger the fairness of the ecosystem. We present Flash Freezing Flash Boys (F3B), a blockchain architecture that addresses front-running attacks by using threshold cryptography. In F3B, a user generates a symmetric key to encrypt their transaction, and once the underlying consensus layer has finalized the transaction, a decentralized secret-management committee reveals this key. F3B mitigates front-running attacks because, before the consensus group finalizes it, an adversary can no longer read the content of a transaction, thus preventing the adversary from benefiting from advanced knowledge of pending transactions. Unlike other mitigation systems, F3B properly ensures that all unfinalized transactions, even with significant delays, remain private by adopting per-transaction protection. Furthermore, F3B addresses front-running at the execution layer; thus, our solution is agnostic to the underlying consensus algorithm and compatible with existing smart contracts. We evaluated F3B on Ethereum with a modified execution layer and found only a negligible (0.026%) increase in transaction latency, specifically due to running threshold decryption with a 128-member secret-management committee after a transaction is finalized; this indicates that F3B is both practical and low-cost.

In-network devices around the world monitor and tamper with connections for many reasons, including intrusion prevention, combating spam or phishing, and country-level censorship. Connection tampering seeks to block access to specific domain names or keywords, and it affects billions of users worldwide with little-to-no transparency. To detect, diagnose, and measure connection-level blocking, "active" measurement techniques originate queries with domains or keywords believed to be blocked and send them from vantage points within networks of interest. Active measurement efforts have been critical to understanding how traffic tampering occurs, but they inherently are unable to capture critical parts of the picture. For instance, knowing the set of domains in a block-list (i.e., what could get blocked) is not the same as knowing what real users are actively experiencing (i.e., what is actively getting blocked).

Decades of work on censorship evasion have resulted in myriad ways to empower clients with the ability to access censored content, but to our knowledge all of them have required some degree of client-side participation. Having to download and run anti-censorship software can put users at risk, and does not help the many users who do not even realize they are being censored in the first place.