Abstract
When we design a security protocol we assume that the humans (or organizations) playing Alice and Bob do not make a difference. In particular, their financial capacity seems to be irrelevant.
In the latest trend to guarantee that secure multi-party computation protocols are fair and not vulnerable to malicious aborts, a slate of protocols has been proposed based on penalty mechanisms. We look at two well-known penalty mechanisms, and show that the so-called see-saw mechanism (Kumaresan et al., CCS 15), is only fit for people with deep pockets, well beyond the stake in the multi-party computation itself.
Depending on the scheme, fairness is not affordable by everyone which has several policy implications on protocol design. To explicitly capture the above issues, we introduce a new property called financial fairness.
In the latest trend to guarantee that secure multi-party computation protocols are fair and not vulnerable to malicious aborts, a slate of protocols has been proposed based on penalty mechanisms. We look at two well-known penalty mechanisms, and show that the so-called see-saw mechanism (Kumaresan et al., CCS 15), is only fit for people with deep pockets, well beyond the stake in the multi-party computation itself.
Depending on the scheme, fairness is not affordable by everyone which has several policy implications on protocol design. To explicitly capture the above issues, we introduce a new property called financial fairness.
| Original language | English |
|---|---|
| Title of host publication | Security Protocols XXVII |
| Subtitle of host publication | 27th International Workshop, Cambridge, UK, April 10–12, 2019, Revised Selected Papers |
| Editors | Jonathan Anderson, Frank Stajano, Bruce Christianson, Vashek Matyáš |
| Publisher | Springer |
| Pages | 135-147 |
| Number of pages | 13 |
| ISBN (Electronic) | 9783030570439 |
| ISBN (Print) | 9783030570422 |
| DOIs | |
| Publication status | Published - 2020 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 12287 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 16 Peace, Justice and Strong Institutions
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