Alexander Krumpholz, Marthie Grobler, Raj Gaire, Claire Mason, Shanae Burns (CSIRO Data61)

With more devices connected to the internet, collecting and sharing data using the Internet of Things (IoT) is an exciting prospect for many food supply chain stakeholders and consumers. However, new technologies introduce significant real and perceived security and privacy concerns that are hindering broader adoption of these technologies. While many of these risks can be mitigated through advanced privacy preservation technologies and security practices, we hypothesized that participants in primary industry supply chains have limited knowledge of these tools. By investigating perceptions and attitudes towards data sharing and privacy preserving tools, we hoped to reveal how communication strategies could be targeted to address this barrier to usable security in data sharing and digital food supply chains. To this end, we carried out pilot interviews and conducted a survey of Australian food supply chain stakeholders to explore: (1) current data sharing practices and the attitudes of food supply chains participants towards such practices, and (2) the perception towards privacy preserving techniques. We found that the extent of data sharing differs among different food supply chains. In general, participants in these supply chains were cautiously positive about the potential for data sharing. They also report that they were developing more trust in privacy preserving technologies as a tool for managing data sharing risk. An issue that emerged was the perception that the effort required to engage with data sharing platforms outweighs the benefits derived from them. Furthermore, the benefits of data sharing were not seen to be evenly distributed across the supply chain. These findings provide useful direction for progressing the adoption of digital supply chains.

View More Papers

Trust the Crowd: Wireless Witnessing to Detect Attacks on...

Kai Jansen (Ruhr University Bochum), Liang Niu (New York University), Nian Xue (New York University), Ivan Martinovic (University of Oxford), Christina Pöpper (New York University Abu Dhabi)

Read More

WATSON: Abstracting Behaviors from Audit Logs via Aggregation of...

Jun Zeng (National University of Singapore), Zheng Leong Chua (Independent Researcher), Yinfang Chen (National University of Singapore), Kaihang Ji (National University of Singapore), Zhenkai Liang (National University of Singapore), Jian Mao (Beihang University)

Read More

Understanding and Detecting International Revenue Share Fraud

Merve Sahin (SAP Security Research), Aurélien Francillon (EURECOM)

Read More

FlowLens: Enabling Efficient Flow Classification for ML-based Network Security...

Diogo Barradas (INESC-ID, Instituto Superior Técnico, Universidade de Lisboa), Nuno Santos (INESC-ID, Instituto Superior Técnico, Universidade de Lisboa), Luis Rodrigues (INESC-ID, Instituto Superior Técnico, Universidade de Lisboa), Salvatore Signorello (LASIGE, Faculdade de Ciências, Universidade de Lisboa), Fernando M. V. Ramos (INESC-ID, Instituto Superior Técnico, Universidade de Lisboa), André Madeira (INESC-ID, Instituto Superior Técnico, Universidade de…

Read More