Privacy-Preserving Data Linkage Across Private and Public Datasets for Collaborative Agriculture Research

Digital agriculture leverages technology to enhance crop yield, disease resilience, and soil health, playing a critical role in agricultural research. However, it raises privacy concerns such as adverse pricing, price discrimination, higher insurance costs, and manipulation of resources, deterring farm operators from sharing data due to potential misuse. This study introduces a privacy-preserving framework that addresses these risks while allowing secure data sharing for digital agriculture. Our framework enables comprehensive data analysis while protecting privacy. It allows stakeholders to harness research-driven policies that link public and private datasets. The proposed algorithm achieves this by: (1) identifying similar farmers based on private datasets, (2) providing aggregate information like time and location, (3) determining trends in price and product availability, and (4) correlating trends with public policy data, such as food insecurity statistics. We validate the framework with real-world Farmer's Market datasets, demonstrating its efficacy through machine learning models trained on linked privacy-preserved data. The results support policymakers and researchers in addressing food insecurity and pricing issues. This work significantly contributes to digital agriculture by providing a secure method for integrating and analyzing data, driving advancements in agricultural technology and development.

Privacy-Preserving Optimal Parameter Selection for Collaborative Clustering

This study investigates the optimal selection of parameters for collaborative clustering while ensuring data privacy. We focus on key clustering algorithms within a collaborative framework, where multiple data owners combine their data. A semi-trusted server assists in recommending the most suitable clustering algorithm and its parameters. Our findings indicate that the privacy parameter ($\epsilon$) minimally impacts the server's recommendations, but an increase in $\epsilon$ raises the risk of membership inference attacks, where sensitive information might be inferred. To mitigate these risks, we implement differential privacy techniques, particularly the Randomized Response mechanism, to add noise and protect data privacy. Our approach demonstrates that high-quality clustering can be achieved while maintaining data confidentiality, as evidenced by metrics such as the Adjusted Rand Index and Silhouette Score. This study contributes to privacy-aware data sharing, optimal algorithm and parameter selection, and effective communication between data owners and the server.

Topic-based Watermarks for LLM-Generated Text

Recent advancements of large language models (LLMs) have resulted in indistinguishable text outputs comparable to human-generated text. Watermarking algorithms are potential tools that offer a way to differentiate between LLM- and human-generated text by embedding detectable signatures within LLM-generated output. However, current watermarking schemes lack robustness against known attacks against watermarking algorithms. In addition, they are impractical considering an LLM generates tens of thousands of text outputs per day and the watermarking algorithm needs to memorize each output it generates for the detection to work. In this work, focusing on the limitations of current watermarking schemes, we propose the concept of a "topic-based watermarking algorithm" for LLMs. The proposed algorithm determines how to generate tokens for the watermarked LLM output based on extracted topics of an input prompt or the output of a non-watermarked LLM. Inspired from previous work, we propose using a pair of lists (that are generated based on the specified extracted topic(s)) that specify certain tokens to be included or excluded while generating the watermarked output of the LLM. Using the proposed watermarking algorithm, we show the practicality of a watermark detection algorithm. Furthermore, we discuss a wide range of attacks that can emerge against watermarking algorithms for LLMs and the benefit of the proposed watermarking scheme for the feasibility of modeling a potential attacker considering its benefit vs. loss.

Protecting Sensitive Data through Federated Co-Training

In many critical applications, sensitive data is inherently distributed. Federated learning trains a model collaboratively by aggregating the parameters of locally trained models. This avoids exposing sensitive local data. It is possible, though, to infer upon the sensitive data from the shared model parameters. At the same time, many types of machine learning models do not lend themselves to parameter aggregation, such as decision trees, or rule ensembles. It has been observed that in many applications, in particular healthcare, large unlabeled datasets are publicly available. They can be used to exchange information between clients by distributed distillation, i.e., co-regularizing local training via the discrepancy between the soft predictions of each local client on the unlabeled dataset. This, however, still discloses private information and restricts the types of models to those trainable via gradient-based methods. We propose to go one step further and use a form of federated co-training, where local hard labels on the public unlabeled datasets are shared and aggregated into a consensus label. This consensus label can be used for local training by any supervised machine learning model. We show that this federated co-training approach achieves a model quality comparable to both federated learning and distributed distillation on a set of benchmark datasets and real-world medical datasets. It improves privacy over both approaches, protecting against common membership inference attacks to the highest degree. Furthermore, we show that federated co-training can collaboratively train interpretable models, such as decision trees and rule ensembles, achieving a model quality comparable to centralized training.

AUTOLYCUS: Exploiting Explainable AI (XAI) for Model Extraction Attacks against Decision Tree Models

Model extraction attack is one of the most prominent adversarial techniques to target machine learning models along with membership inference attack and model inversion attack. On the other hand, Explainable Artificial Intelligence (XAI) is a set of techniques and procedures to explain the decision making process behind AI. XAI is a great tool to understand the reasoning behind AI models but the data provided for such revelation creates security and privacy vulnerabilities. In this poster, we propose AUTOLYCUS, a model extraction attack that exploits the explanations provided by LIME to infer the decision boundaries of decision tree models and create extracted surrogate models that behave similar to a target model.

Key Protected Classification for Collaborative Learning

Large-scale datasets play a fundamental role in training deep learning models. However, dataset collection is difficult in domains that involve sensitive information. Collaborative learning techniques provide a privacy-preserving solution, by enabling training over a number of private datasets that are not shared by their owners. However, recently, it has been shown that the existing collaborative learning frameworks are vulnerable to an active adversary that runs a generative adversarial network (GAN) attack. In this work, we propose a novel classification model that is resilient against such attacks by design. More specifically, we introduce a key-based classification model and a principled training scheme that protects class scores by using class-specific private keys, which effectively hides the information necessary for a GAN attack. We additionally show how to utilize high dimensional keys to improve the robustness against attacks without increasing the model complexity. Our detailed experiments demonstrate the effectiveness of the proposed technique.

BPRS: Belief Propagation Based Iterative Recommender System

In this paper we introduce the first application of the Belief Propagation (BP) algorithm in the design of recommender systems. We formulate the recommendation problem as an inference problem and aim to compute the marginal probability distributions of the variables which represent the ratings to be predicted. However, computing these marginal probability functions is computationally prohibitive for large-scale systems. Therefore, we utilize the BP algorithm to efficiently compute these functions. Recommendations for each active user are then iteratively computed by probabilistic message passing. As opposed to the previous recommender algorithms, BPRS does not require solving the recommendation problem for all the users if it wishes to update the recommendations for only a single active. Further, BPRS computes the recommendations for each user with linear complexity and without requiring a training period. Via computer simulations (using the 100K MovieLens dataset), we verify that BPRS iteratively reduces the error in the predicted ratings of the users until it converges. Finally, we confirm that BPRS is comparable to the state of art methods such as Correlation-based neighborhood model (CorNgbr) and Singular Value Decomposition (SVD) in terms of rating and precision accuracy. Therefore, we believe that the BP-based recommendation algorithm is a new promising approach which offers a significant advantage on scalability while providing competitive accuracy for the recommender systems.