Praveen Gauravaram

Agriculture technology (AgTech) applies emerging technologies, such as the Internet of Things (IoT), sensors, and artificial intelligence to collect, transmit, and process information related to agricultural enterprises. AgTech solutions involve many interconnected systems, each with a range of security vulnerabilities that may impact data trustworthiness. To engage AgTech effectively, a framework is necessary for providing assurances that data used for decision making can be trusted. This article introduces a novel framework “AgTrust” that provides a cohesive solution for enabling trust in farming ecosystems. This includes assurances of data integrity, data quality, and data provenance supported by anomaly detection, quantum-safe encryption, authentication, and the underlying key-management mechanisms. The AgTrust framework is a modular design, intended for flexibility to enable implementation in a range of context and to meet the challenges of a rapidly evolving threat landscape using appropriate security mechanisms to ensure data trustworthiness. The modules providing various security functions can be adapted to future-proof the AgTech development and can be applied to other IoT or smart-device-based networks. The AgTrust framework specifies the functionality required across various layers and services and coordinates interactions between functions. Four case study examples related to the implementation of the AgTrust framework in real-world scenarios are presented and discussed.

Digital agriculture [agriculture technology (AgTech)] applies emerging technologies, such as the Internet of Things, sensors, and artificial intelligence, to collect, transmit, and process information related to agricultural enterprises. Potential benefits of AgTech are improved productivity, supply chain efficiency, food safety, and the sustainable use of natural resources, contributing to the resilience of farming practices. While the benefits of AgTech use are clear, cyber risks are introduced with this technology that require effective management. Farmers need confidence that the data gained through the use of AgTech are trustworthy (accurate, reliable, and authentic). Data trustworthiness is critical for quality data-informed decision making. Poor quality data may impact agricultural production and negate any potential benefits gained. This article outlines risks related to data trustworthiness for a diverse audience, including those with an agricultural background and/or a technical background in communication and network security, and for the end users (i.e., farmers). This article also discusses the threats associated with AgTech and provides six scenarios that identify potential cyberthreat actors, possible motivations, attack vectors, compromised security goals, and the impact on farm businesses.

The widespread integration of the Internet of Things (IoT) is crucial in advancing sustainable development. IoT service providers actively collect user data for analysis using sophisticated Deep Learning (DL) algorithms. This enables the extraction of valuable insights for business intelligence and improving service quality. However, as these datasets contain sensitive personal information, there is a risk of privacy breaches when DL models are employed. This vulnerability may result in Membership Inference Attacks (MIA), potentially leading to the unauthorized disclosure of highly sensitive data. Therefore, developing an efficient and privacy-preserving data analysis system for IoT is imperative. Recent research has highlighted the effectiveness of utilizing Bloom Filter (BF)-encoding in conjunction with Differential Privacy (DP) for safeguarding privacy during data analysis. Given its attributes of low complexity and high utility, this approach proves effective, particularly in resource-constrained IoT domains. With this in mind, we propose a novel framework for privacy-preserving IoT data analysis based on BF-encoded data. Our research introduces an innovative BF-encoding technique combined with Local Differential Privacy (LDP), capable of efficiently encoding various types of IoT data (such as facial images and smart-meter data) while maintaining privacy when integrated into DL algorithms for downstream analysis. Experimental results demonstrate that our BF-encoded data surpasses the utility of standard BF-encoded data when utilized in DL algorithms for downstream tasks, showcasing an approximate 30% improvement in classification accuracy. Furthermore, we assess the privacy of these DL models against MIA, revealing that attackers can only make random guesses with an accuracy of approximately 50%.

Implementing strong authentication methods in a network requires stable connectivity between the service providers deployed within the network (i.e., applications that users of the network need to access) and the Identity and Access Management (IAM) server located at the core segment of the network. This becomes challenging when it comes to Operational Technology (OT) systems deployed in a remote area, as they often get disconnected from the core segment of the network owing to unavoidable network disruptions. As a result, weak authentication methods and shared credential approaches are still adopted in these OT environments, exposing system vulnerabilities to increasingly sophisticated cyber threats. In this work, we propose a solution to enable highly available multi-factor authentication (MFA) services for OT environments. The proposed solution is based on Proof-of-Possession (PoP) tokens generated by an IAM server for registered users. The tokens are securely linked to user-specific parameters (e.g., physical security keys, biometrics, PIN, etc.), enabling strong user authentication (during disconnection time) through token validation. We deployed the Tamarin Prover software-based toolkit to verify security of the proposed authentication scheme. For performance evaluation, we implemented the designed solution in real-world settings. The results of our analysis and experiments confirm the efficacy of the proposed solution.