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Private set intersection (PSI) allows two mutually distrusting parties each holding a private set of elements, to learn the intersection of their sets without revealing anything beyond the intersection. Recent work (Badrinarayanan et al., PoPETS’22) initiates the study of updatable PSI (UPSI), which allows the two parties to compute PSI on a regular basis with sets that constantly get updated, where both the computation and communication complexity only grow with the size of the small updates and not the large entire sets. However, there are several limitations of their presented protocols. First, they can only be used to compute the plain PSI functionality and do not support extended functionalities such as PSI-Cardinality and PSI-Sum. Second, they only allow parties to add new elements to their existing set and do not support arbitrary deletion of elements. Finally, their addition-only protocols either require both parties to learn the output or only achieve low complexity in an amortized sense and incur linear worst-case complexity. In this work, we address all the above limitations. In particular, we study UPSI with semi-honest security in both the addition-only and addition-deletion settings. We present new protocols for both settings that support plain PSI as well as extended functionalities including PSI-Cardinality and PSI-Sum, achieving one-sided output (which implies two-sided output). In the addition-only setting, we also present a protocol for a more general functionality Circuit-PSI that outputs secret shares of the intersection. All of our protocols have worst-case computation and communication complexity that only grow with the set updates instead of the entire sets (except for a polylogarithmic factor). We implement our new UPSI protocols and compare with the state-of-the-art protocols for PSI and extended functionalities. Our protocols compare favorably when the total set sizes are sufficiently large, the new updates are sufficiently small, or in networks with low bandwidth. 

Privacy-preserving machine learning (PPML) enables multiple distrusting parties to jointly train ML models on their private data without revealing any information beyond the final trained models. In this work, we study the client-aided two-server setting where two non-colluding servers jointly train an ML model on the data held by a large number of clients. By involving the clients in the training process, we develop efficient protocols for training algorithms including linear regression, logistic regression, and neural networks. In particular, we introduce novel approaches to securely computing inner product, sign check, activation functions (e.g., ReLU, logistic function), and division on secret shared values, leveraging lightweight computation on the client side. We present constructions that are secure against semi-honest clients and further enhance them to achieve security against malicious clients. We believe these new client-aided techniques may be of independent interest. We implement our protocols and compare them with the two-server PPML protocols presented in SecureML (Mohassel and Zhang, S&P’17) across various settings and ABY2.0 (Patra et al., Usenix Security’21) theoretically. We demonstrate that with the assistance of untrusted clients in the training process, we can significantly improve both the communication and computational efficiency by orders of magnitude. Our protocols compare favorably in all the training algorithms on both LAN and WAN networks. 

Abstract Spray‐induced gene silencing (SIGS) is an emerging tool for crop pest protection. It utilizes exogenously applied double‐stranded RNA to specifically reduce pest target gene expression using endogenous RNA interference machinery. In this study, SIGS methods were developed and optimized for powdery mildew fungi, which are widespread obligate biotrophic fungi that infect agricultural crops, using the known azole‐fungicide targetcytochrome P45051 (CYP51) in theGolovinomyces orontii–Arabidopsis thalianapathosystem. Additional screening resulted in the identification of conserved gene targets and processes important to powdery mildew proliferation:apoptosis‐antagonizing transcription factorin essential cellular metabolism and stress response; lipid catabolism geneslipase a,lipase 1, andacetyl‐CoA oxidasein energy production;and genes involved in manipulation of the plant host via abscisic acid metabolism (9‐cis‐epoxycarotenoid dioxygenase,xanthoxin dehydrogenase, and a putativeabscisic acid G‐protein coupled receptor) and secretion of the effector protein,effector candidate 2. Powdery mildew is the dominant disease impacting grapes and extensive powdery mildew resistance to applied fungicides has been reported. We therefore developed SIGS for theErysiphe necator–Vitis viniferasystem and tested six successful targets identified using theG. orontii–A. thalianasystem. For all targets tested, a similar reduction in powdery mildew disease was observed between systems. This indicates screening of broadly conserved targets in theG. orontii–A. thalianapathosystem identifies targets and processes for the successful control of other powdery mildew fungi. The efficacy of SIGS on powdery mildew fungi makes SIGS an exciting prospect for commercial powdery mildew control.