State of Alaska Election Security Project Phase 2 Report

A laska’s election system is among the most secure in the country, and it has a number of safeguards other states are now adopting. But the technology Alaska uses to record and count votes could be improved— and the state’s huge size, limited road system, and scattered communities also create special challenges for insuring the integrity of the vote. In this second phase of an ongoing study of Alaska’s election security, we recommend ways of strengthening the system—not only the technology but also the election procedures. The lieutenant governor and the Division of Elections asked the University of Alaska Anchorage to do this evaluation, which began in September 2007.Lieutenant Governor Sean Parnell. State of Alaska Division of Elections.List of Appendices / Glossary / Study Team / Acknowledgments / Introduction / Summary of Recommendations / Part 1 Defense in Depth / Part 2 Fortification of Systems / Part 3 Confidence in Outcomes / Conclusions / Proposed Statement of Work for Phase 3: Implementation / Reference

Applying Block Chain Technologies to Digital Voting Algorithms

Voting is a fundamental aspect to democracy. Many countries have advanced voting systems in place, but many of these systems have issues behind them such as not being anonymous or verifiable. Additionally, most voting systems currently have a central authority in charge of counting votes, which can be prone to corruption. We propose a voting system which mitigates many of these issues. Our voting system attempts to provide decentralization, pseudoanonymity, and verifiability. For our system, we have identified the requirements, implemented the backbone of the system, recognized some of its shortcomings, and proposed areas of future work on this voting system

What proof do we prefer? Variants of verifiability in voting

In this paper, we discuss one particular feature of Internet voting, verifiability, against the background of scientific literature and experiments in the Netherlands. In order to conceptually clarify what verifiability is about, we distinguish classical verifiability from constructive veriability in both individual and universal verification. In classical individual verifiability, a proof that a vote has been counted can be given without revealing the vote. In constructive individual verifiability, a proof is only accepted if the witness (i.e. the vote) can be reconstructed. Analogous concepts are de- fined for universal veriability of the tally. The RIES system used in the Netherlands establishes constructive individual verifiability and constructive universal verifiability, whereas many advanced cryptographic systems described in the scientific literature establish classical individual verifiability and classical universal verifiability. If systems with a particular kind of verifiability continue to be used successfully in practice, this may influence the way in which people are involved in elections, and their image of democracy. Thus, the choice for a particular kind of verifiability in an experiment may have political consequences. We recommend making a well-informed democratic choice for the way in which both individual and universal verifiability should be realised in Internet voting, in order to avoid these unconscious political side-effects of the technology used. The safest choice in this respect, which maintains most properties of current elections, is classical individual verifiability combined with constructive universal verifiability. We would like to encourage discussion about the feasibility of this direction in scientific research

Explanation and trust: what to tell the user in security and AI?

There is a common problem in artificial intelligence (AI) and information security. In AI, an expert system needs to be able to justify and explain a decision to the user. In information security, experts need to be able to explain to the public why a system is secure. In both cases, the goal of explanation is to acquire or maintain the users' trust. In this paper, we investigate the relation between explanation and trust in the context of computing science. This analysis draws on literature study and concept analysis, using elements from system theory as well as actor-network theory. We apply the conceptual framework to both AI and information security, and show the benefit of the framework for both fields by means of examples. The main focus is on expert systems (AI) and electronic voting systems (security). Finally, we discuss consequences of our analysis for ethics in terms of (un)informed consent and dissent, and the associated division of responsibilities