Alexandre Yersin's explorations (1892-1894) in French Indochina before the discovery of the plague bacillus.

Alexandre Yersin, the great French discoverer of yersinia pestis, was a keen explorer of unknown lands. At the age of 30, a member of the French Colonial Health service, he set off to fulfil his intimate dream and explore other continents. For almost two years and three long expeditions, he journeyed through widely unknown regions in the province of the French Indochina, in southeast Asia, territories of Vietnam, Cambodia and Laos. This article presents vignettes from his explorations. During his difficult travels, he carefully planned and noted his itineraries; designed new routes, but also observed and recorded sociodemographic and environmental data and unidentified diseases. The immature science of late 19th century geography had the strength to allure such an influential medical figure and place him among the early medical geographers. His journeys, observations and recordings brought to Yersin great experience, and he made his most important scientific contributions after he had concluded his explorations

Incentivizing Participation in Crowd-Sensing Applications Through Fair and Private Bitcoin Rewards

In this work we develop a rewarding framework that can be used to enhance existing crowd-sensing applications. Although a core requirement of such systems is user engagement, people may be reluctant to participate because sensitive information about them may be leaked or inferred from submitted data. The use of monetary rewards can help incentivize participation, thereby increasing not only the amount but also the quality of sensed data. Our framework allows users to submit data and obtain Bitcoin payments in a privacy-preserving manner, preventing curious providers from linking the data or the payments back to the user. At the same time, it prevents malicious user behavior such as double-redeeming attempts, where a user tries to obtain rewards for multiple submissions of the same data. More importantly, it ensures the fairness of the exchange in a completely trustless manner; by relying on the Blockchain, the trust placed on third parties in traditional fair exchange protocols is eliminated. Finally, our system is highly efficient as most of the protocol steps do not utilize the Blockchain network. When they do, only the simplest of Blockchain transactions are used as opposed to prior works that are based on the use of more complex smart contracts.publishedVersionPeer reviewe

Private Lives Matter: A Differential Private Functional Encryption Scheme (extended version)

The use of data combined with tailored statistical analysis have presented a unique opportunity to organizations in diverse fields to observe users\u27 behaviors and needs, and accordingly adapt and fine-tune their services. However, in order to offer utilizable, plausible, and personalized alternatives to users, this process usually also entails a breach of their privacy. The use of statistical databases for releasing data analytics is growing exponentially, and while many cryptographic methods are utilized to protect the confidentiality of the data -- a task that has been ably carried out by many authors over the years -- only a few %rudimentary number of works focus on the problem of privatizing the actual databases. Believing that securing and privatizing databases are two equilateral problems, in this paper, we propose a hybrid approach by combining Functional Encryption with the principles of Differential Privacy. Our main goal is not only to design a scheme for processing statistical data and releasing statistics in a privacy-preserving way but also to provide a richer, more balanced, and comprehensive approach in which data analytics and cryptography go hand in hand with a shift towards increased privacy

FE[r]Chain: Enforcing Fairness in Blockchain Data Exchanges Through Verifiable Functional Encryption

Functional Encryption (FE) allows users to extract specific function-related information from encrypted data while preserving the privacy of the underlying plaintext. Though significant research has been devoted to developing secure and efficient Multi-Input Functional Encryption schemes supporting diverse functions, there remains a noticeable research gap in the development of verifiable FE schemes. Functionality and performance have received considerable attention, however, the crucial aspect of verifiability in FE has been relatively understudied. Another important aspect that prior research in FE with outsourced decryption has not adequately addressed is the fairness of the data-for-money exchange between a curator and an analyst. This paper focuses on addressing these gaps by proposing a verifiable FE scheme for inner product computation. The scheme not only supports the multi-client setting but also extends its functionality to accommodate multiple users -- an essential feature in modern privacy-respecting services. Additionally, it demonstrates how this FE scheme can be effectively utilized to ensure fairness and atomicity in a payment protocol, further enhancing the trustworthiness of data exchanges

Multi-party trust computation in decentralized environments in the presence of malicious adversaries

In this paper, we describe a decentralized privacy-preserving protocol for securely casting trust ratings in distributed reputation systems. Our protocol allows n participants to cast their votes in a way that preserves the privacy of individual values against both internal and external attacks. The protocol is coupled with an extensive theoretical analysis in which we formally prove that our protocol is resistant to collusion against as many as n-1 corrupted nodes in both the semi-honest and malicious adversarial models. The behavior of our protocol is tested in a real P2P network by measuring its communication delay and processing overhead. The experimental results uncover the advantages of our protocol over previous works in the area; without sacrificing security, our decentralized protocol is shown to be almost one order of magnitude faster than the previous best protocol for providing anonymous feedback

Secure and scalable deduplication of horizontally partitioned health data for privacy-preserving distributed statistical computation

Background Techniques have been developed to compute statistics on distributed datasets without revealing private information except the statistical results. However, duplicate records in a distributed dataset may lead to incorrect statistical results. Therefore, to increase the accuracy of the statistical analysis of a distributed dataset, secure deduplication is an important preprocessing step. Methods We designed a secure protocol for the deduplication of horizontally partitioned datasets with deterministic record linkage algorithms. We provided a formal security analysis of the protocol in the presence of semi-honest adversaries. The protocol was implemented and deployed across three microbiology laboratories located in Norway, and we ran experiments on the datasets in which the number of records for each laboratory varied. Experiments were also performed on simulated microbiology datasets and data custodians connected through a local area network. Results The security analysis demonstrated that the protocol protects the privacy of individuals and data custodians under a semi-honest adversarial model. More precisely, the protocol remains secure with the collusion of up to N − 2 corrupt data custodians. The total runtime for the protocol scales linearly with the addition of data custodians and records. One million simulated records distributed across 20 data custodians were deduplicated within 45 s. The experimental results showed that the protocol is more efficient and scalable than previous protocols for the same problem. Conclusions The proposed deduplication protocol is efficient and scalable for practical uses while protecting the privacy of patients and data custodians

Invasive lionfish in the Mediterranean: Low public awareness yet high stakeholder concerns

A lionfish invasion in the Western Atlantic has been one of the most ecologically harmful fish invasions to date. Experience there has shown that its management is most effective when the public and stakeholders are involved. The lionfish (Pterois miles) has recently invaded the Mediterranean, spreading at an alarming rate. To understand lionfish knowledge and perceptions, questionnaire surveys were conducted with a representative cross section of the adult general public (via telephone) and stakeholders (via organised meetings) in Cyprus. Results from 300 public surveys revealed limited awareness about the lionfish but strong support for its local management. Men and older respondents showed stronger support compared to women and younger respondents, respectively. Results from 108 stakeholder revealed high level of awareness and almost unanimous support for management measures. The majority had not experienced any effects from the recent lionfish invasion, but some reported negative impacts such as limited access to dive sites, ecosystem damage and fishing gear destruction. Few stakeholders perceived benefits of this invasive species, e.g. to dive tourism or as a food source. Almost all stakeholders expressed a willingness to get involved in lionfish management, but only around half would consider personal consumption, or sports incentives as good incentives for their participation. Encouragement from scientists through coordination , training and support was suggested as an essential part of effective management strategy. The results of this study can inform an efficient adaptive management process across the Mediterranean region and assist future engagement of citizen scientists in lionfish control and mitigation

Practical Energy Harvesting for Batteryless Ambient Backscatter Sensors

This work studies the performance of two methods for providing power to an ultra-low power, ambient backscatter tag, omitting the need for any battery. RF energy harvesting from a dedicated source and energy harvesting from ambient light using a single photodiode are compared. Extensive measurement results from tests conducted under real world conditions are offered for both harvesting methods. It is concluded that for a total cost of under 7 Euros the need for a battery can be eliminated, by using a single photodiode element along with a suitable boost converter. The ultra-low power character of the utilized tag enables the use of multiple harvesting methods and paves the way towards truly battery-less wireless sensor systems