Entrada to Secure Graph Convolutional Networks

Graph convolutional networks (GCNs) are gaining popularity due to their powerful modelling capabilities. However, guaranteeing privacy is an issue when evaluating on inputs that contain users’ sensitive information such as financial transactions, medical records, etc. To address such privacy concerns, we design Entrada, a framework for securely evaluating GCNs that relies on the technique of secure multiparty computation (MPC). For efficiency and accuracy reasons, Entrada builds over the MPC framework of Tetrad (NDSS’22) and enhances the same by providing the necessary primitives. Moreover, Entrada leverages the GraphSC paradigm of Araki et al. (CCS’21) to further enhance efficiency. This entails designing a secure and efficient shuffle protocol specifically in the 4-party setting, which to the best of our knowledge, is done for the first time and may be of independent interest. Through extensive experiments, we showcase that the accuracy of secure GCN evaluated via Entrada is on par with its cleartext counterpart. We also benchmark efficiency of Entrada with respect to the included primitives as well as the framework as a whole. Finally, we showcase Entrada’s practicality by benchmarking GCN-based fraud detection application

Shield: Secure Allegation Escrow System with Stronger Guarantees

The rising issues of harassment, exploitation, corruption, and other forms of abuse have led victims to seek comfort by acting in unison against common perpetrators (e.g., #MeToo movement). One way to curb these issues is to install allegation escrow systems that allow victims to report such incidents. The escrows are responsible for identifying victims of a common perpetrator and taking the necessary action to bring justice to them. However, users hesitate to participate in these systems due to the fear of such sensitive reports being leaked to perpetrators, who may further misuse them. Thus, to increase trust in the system, cryptographic solutions are being designed to realize secure allegation escrow (SAE) systems. In the work of Arun et al. (NDSS\u2720), which presents the state-of-the-art solution, we identify attacks that can leak sensitive information and compromise victim privacy. We also report issues present in prior works that were left unidentified. To arrest all these breaches, we put forth an SAE system that prevents the identified attacks and retains the salient features from all prior works. The cryptographic technique of secure multi-party computation (MPC) serves as the primary underlying tool in designing our system. At the heart of our system lies a new duplicity check protocol and an improved matching protocol. We also provide additional features such as allegation modification and deletion, which were absent in the state of the art. To demonstrate feasibility, we benchmark the proposed system with state-of-the-art MPC protocols and report the cost of processing an allegation. Different settings that affect system performance are analyzed, and the reported values showcase the practicality of our solution

PentaGOD: Stepping beyond Traditional GOD with Five Parties

Secure multiparty computation (MPC) is increasingly being used to address privacy issues in various applications. The recent work of Alon et al. (CRYPTO\u2720) identified the shortcomings of traditional MPC and defined a Friends-and-Foes (FaF) security notion to address the same. We showcase the need for FaF security in real-world applications such as dark pools. This subsequently necessitates designing concretely efficient FaF-secure protocols. Towards this, keeping efficiency at the center stage, we design ring-based FaF-secure MPC protocols in the small-party honest-majority setting. Specifically, we provide (1,1)-FaF secure 5 party computation protocols (5PC) that consider one malicious and one semi-honest corruption and constitutes the optimal setting for attaining honest-majority. At the heart of it lies the multiplication protocol that requires a single round of communication with 8 ring elements (amortized). To facilitate having FaF-secure variants for several applications, we design a variety of building blocks optimized for our FaF setting. The practicality of the designed (1,1)-FaF secure 5PC framework is showcased by benchmarking dark pools. In the process, we also improve the efficiency and security of the dark pool protocols over the existing traditionally secure ones. This improvement is witnessed as a gain of up to $62\times$ in throughput compared to the existing ones. Finally, to demonstrate the versatility of our framework, we also benchmark popular deep neural networks

Find Thy Neighbourhood: Privacy-Preserving Local Clustering

Identifying a cluster around a seed node in a graph, termed local clustering, finds use in several applications, including fraud detection, targeted advertising, community detection, etc. However, performing local clustering is challenging when the graph is distributed among multiple data owners, which is further aggravated by the privacy concerns that arise in disclosing their view of the graph. This necessitates designing solutions for privacy-preserving local clustering and is addressed for the first time in the literature. We propose using the technique of secure multiparty computation (MPC) to achieve the same. Our local clustering algorithm is based on the heat kernel PageRank (HKPR) metric, which produces the best-known cluster quality. En route to our final solution, we have two important steps: (i) designing data-oblivious equivalent of the state-of-the-art algorithms for computing local clustering and HKPR values, and (ii) compiling the data-oblivious algorithms into its secure realisation via an MPC framework that supports operations over fixed-point arithmetic representation such as multiplication and division. Keeping efficiency in mind for large graphs, we choose the best-known honest-majority 3-party framework of SWIFT (Koti et al., USENIX\u2721) and enhance it with some of the necessary yet missing primitives, before using it for our purpose. We benchmark the performance of our secure protocols, and the reported run time showcases the practicality of the same. Further, we perform extensive experiments to evaluate the accuracy loss of our protocols. Compared to their cleartext counterparts, we observe that the results are comparable and thus showcase the practicality of the designed protocols

Vogue: Faster Computation of Private Heavy Hitters

Consider the problem of securely identifying τ -heavy hitters, where given a set of client inputs, the goal is to identify those inputs which are held by at least τ clients in a privacy-preserving manner. Towards this, we design a novel system Vogue, whose key highlight in comparison to prior works, is that it ensures complete privacy and does not leak any information other than the heavy hitters. In doing so, Vogue aims to achieve as efficient a solution as possible. To showcase these efficiency improvements, we benchmark our solution and observe that it requires around 14 minutes to compute the heavy hitters for τ = 900 on 256-bit inputs when considering 400K clients. This is in contrast to the state of the art solution that requires over an hour for the same. In addition to the static input setting described above, Vogue also accounts for streaming inputs and provides a protocol that outperforms the state-of-the-art therein. The efficiency improvements witnessed while computing heavy hitters in both, the static and streaming input settings, are attributed to our new secure stable compaction protocol, whose round complexity is independent of the size of the input array to be compacte

Ruffle: Rapid 3-party shuffle protocols

Secure shuffle is an important primitive that finds use in several applications such as secure electronic voting, oblivious RAMs, secure sorting, to name a few. For time-sensitive shuffle-based applications that demand a fast response time, it is essential to design a fast and efficient shuffle protocol. In this work, we design secure and fast shuffle protocols relying on the techniques of secure multiparty computation. We make several design choices that aid in achieving highly efficient protocols. Specifically, we consider malicious 3-party computation setting with an honest majority and design robust ring-based protocols. Our shuffle protocols provide a fast online (i.e., input-dependent) phase compared to the state-of-the-art for the considered setting. To showcase the efficiency improvements brought in by our shuffle protocols, we consider two distinct applications of anonymous broadcast and secure graph computation via the GraphSC paradigm. In both cases, multiple shuffle invocations are required. Hence, going beyond standalone shuffle invocation, we identify two distinct scenarios of multiple invocations and provide customised protocols for the same. Further, we showcase that our customized protocols not only provide a fast response time, but also provide improved overall run time for multiple shuffle invocations. With respect to the applications, we not only improve in terms of efficiency, but also work towards providing improved security guarantees, thereby outperforming the respective state-of-the-art works. We benchmark our shuffle protocols and the considered applications to analyze the efficiency improvements with respect to various parameters