FHE enables computations on encrypted data, making it essential for privacy-preserving applications. However, it involves computationally demanding tasks, such as polynomial multiplication, while NTT is the state-of-the-art solution to perform this task. Most FHE schemes operate over the negacyclic ring of polynomials. We introduce a novel formulation of the hierarchical Four-Step NTT approach for the negacyclic ring, eliminating the need for pre- and post-processing steps found in the...

Protein sequence classification is crucial in many research areas, such as predicting protein structures and discovering new protein functions. Leveraging large language models (LLMs) is greatly promising to enhance our ability to tackle protein sequence classification problems; however, the accompanying privacy issues are becoming increasingly prominent. In this paper, we present a privacy-preserving, non-interactive, efficient, and accurate protocol called encrypted DASHformer to evaluate...

Secure aggregation is the distributed task of securely computing a sum of values (or a vector of values) held by a set of parties, revealing only the output (i.e., the sum) in the computation. Existing protocols, such as Prio (NDSI’17), Prio+ (SCN’22), Elsa (S&P’23), and Whisper (S&P’24), support secure aggregation with input validation to ensure inputs belong to a specified domain. However, when malicious servers are present, these protocols primarily guarantee privacy but not input...

Privacy-preserving graph analysis allows performing computations on graphs that store sensitive information while ensuring all the information about the topology of the graph, as well as data associated with the nodes and edges, remains hidden. The current work addresses this problem by designing a highly scalable framework, $\mathsf{Graphiti}$, that allows securely realising any graph algorithm. $\mathsf{Graphiti}$ relies on the technique of secure multiparty computation (MPC) to design a...

Homomorphic Encryption (HE) technology allows for processing encrypted data, breaking through data isolation barriers and providing a promising solution for privacy-preserving computation. The integration of HE technology into Convolutional Neural Network (CNN) inference shows potential in addressing privacy issues in identity verification, medical imaging diagnosis, and various other applications. The CKKS HE algorithm stands out as a popular option for homomorphic CNN inference due to its...

Top trading cycles (TTC) is a famous algorithm for trading indivisible goods between a set of agents such that all agents are as happy as possible about the outcome. In this paper, we present a protocol for executing TTC in a privacy preserving way. To the best of our knowledge, it is the first of its kind. As a technical contribution of independent interest, we suggest a new algorithm for determining all nodes in a functional graph that are on a cycle. The algorithm is particularly well...

We show that the outsourcing algorithm for the case of linear constraints [IEEE Trans. Cloud Comput., 2023, 11(1), 484-498] cannot keep output privacy, due to the simple translation transformation. We also suggest a remedy method by adopting a hybrid transformation which combines the usual translation transformation and resizing transformation so as to protect the output privacy.

We revisit the problem of Authorized Private Set Intersection (APSI), which allows mutually untrusting parties to authorize their items using a trusted third-party judge before privately computing the intersection. We also initiate the study of Partial-APSI, a novel privacy-preserving generalization of APSI in which the client only reveals a subset of their items to a third-party semi-honest judge for authorization. Partial-APSI allows for partial verification of the set, preserving the...

The proliferation of attacks to cloud computing, coupled with the vast amounts of data outsourced to online services, continues to raise major concerns about the privacy for end users. Traditional cryptography can help secure data transmission and storage on cloud servers, but falls short when the already encrypted data needs to be processed by the cloud provider. An emerging solution to this challenge is fully homomorphic encryption (FHE), which enables computations directly on encrypted...

Homomorphic Encryption (HE) is a cutting-edge cryptographic technique that enables computations on encrypted data to be mirrored on the original data. This has quickly attracted substantial interest from the research community due to its extensive practical applications, such as in cloud computing and privacy-preserving machine learning. In addition to confidentiality, the importance of authenticity has emerged to ensure data integrity during transmission and evaluation. To address...

For more than two decades, pairings have been a fundamental tool for designing elegant cryptosystems, varying from digital signature schemes to more complex privacy-preserving constructions. However, the advancement of quantum computing threatens to undermine public-key cryptography. Concretely, it is widely accepted that a future large-scale quantum computer would be capable to break any public-key cryptosystem used today, rendering today's public-key cryptography obsolete and mandating the...

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,...

Clustering is a crucial unsupervised learning method extensively used in the field of data analysis. For analyzing big data, outsourced computation is an effective solution but privacy concerns arise when involving sensitive information. Fully homomorphic encryption (FHE) enables computations on encrypted data, making it ideal for such scenarios. However, existing privacy-preserving clustering based on FHE are often constrained by the high computational overhead incurred from FHE, typically...

Fully homomorphic encryption (FHE) enables arbitrary computation on encrypted data, but certain applications remain prohibitively expensive in the encrypted domain. As a case in point, comparing two encrypted sets of data is extremely computationally expensive due to the large number of comparison operators required. In this work, we propose a novel methodology for encrypted set similarity inspired by the MinHash algorithm and the CGGI FHE scheme. Doing comparisons in FHE requires...

Fully homomorphic encryption (FHE) has become progressively more viable in the years since its original inception in 2009. At the same time, leveraging state-of-the-art schemes in an efficient way for general computation remains prohibitively difficult for the average programmer. In this work, we introduce a new design for a fully homomorphic processor, dubbed Juliet, to enable faster operations on encrypted data using the state-of-the-art TFHE and cuFHE libraries for both CPU and GPU...

The Internet has plenty of images that are transformations (e.g., resize, blur) of confidential original images. Several scenarios (e.g., selling images over the Internet, fighting disinformation, detecting deep fakes) would highly benefit from systems allowing to verify that an image is the result of a transformation applied to a confidential authentic image. In this paper, we focus on systems for proving and verifying the correctness of transformations of authentic images guaranteeing: 1)...

Privacy is a major concern in large-scale digital applications, such as cloud-computing, machine learning services, and access control. Users want to protect not only their plain data but also their associated attributes (e.g., age, location, etc). Functional encryption (FE) is a cryptographic tool that allows fine-grained access control over encrypted data. However, existing FE fall short as they are either inefficient and far from reality or they leak sensitive user-specific...

With additively homomorphic encryption (AHE), one can compute, from input ciphertexts $\mathsf{Enc}(x_1),\ldots,\mathsf{Enc}(x_n)$, and additional inputs $y_1,\ldots,y_k$, a ciphertext $c_\textit{f}=\mathsf{Enc}(f(x_1,\ldots,x_n,y_1,\ldots, y_k))$ for any polynomial $f$ in which each monomial has total degree at most $1$ in the $x$-variables (but can be arbitrary in the $y$-variables). For AHE that satisfies a set of natural requirements, we give a non-interactive zero-knowledge proof...

We present Whisper, a system for privacy-preserving collection of aggregate statistics. Like prior systems, a Whisper deployment consists of a small set of non-colluding servers; these servers compute aggregate statistics over data from a large number of users without learning the data of any individual user. Whisper’s main contribution is that its server- to-server communication cost and its server-side storage costs scale sublinearly with the total number of users. In particular, prior...

At S$\&$P 2023, a family of secure three-party computing protocols called Bicoptor was proposed by Zhou et al., which is used to compute non-linear functions in privacy preserving machine learning. In these protocols, two parties $P_0, P_1$ respectively hold the corresponding shares of the secret, while a third party $P_2$ acts as an assistant. The authors claimed that neither party in the Bicoptor can independently compromise the confidentiality of the input, intermediate, or output. In...

With the growing adoption of cloud computing, the ability to store data and delegate computations to powerful and affordable cloud servers have become advantageous for both companies and individual users. However, the security of cloud computing has emerged as a significant concern. Particularly, Cloud Service Providers (CSPs) cannot assure data confidentiality and computations integrity in mission-critical applications. In this paper, we propose a confidential and verifiable delegation...

Secure multi-party computation aims to allow a set of players to compute a given function on their secret inputs without revealing any other information than the result of the computation. In this work, we focus on the design of secure multi-party protocols for shared polynomial operations. We consider the classical model where the adversary is honest-but-curious, and where the coefficients (or any secret values) are either encrypted using an additively homomorphic encryption scheme or...

In recent years, decentralized computing has gained popularity in various domains such as decentralized learning, financial services and the Industrial Internet of Things. As identity privacy becomes increasingly important in the era of big data, safeguarding user identity privacy while ensuring the security of decentralized computing systems has become a critical challenge. To address this issue, we propose ADC (Anonymous Decentralized Computing) to achieve anonymity in decentralized...

The computation of step functions over encrypted data is an essential issue in homomorphic encryption due to its fundamental application in privacy-preserving computing. However, an effective method for homomorphically computing general step functions remains elusive in cryptography. This paper proposes two polynomial approximation methods for general step functions to tackle this problem. The first method leverages the fact that any step function can be expressed as a linear combination of...

Homomorphic encryption is a powerful privacy-preserving technology that is notoriously difficult to configure and use, even for experts. The key difficulties include restrictive programming models of homomorphic schemes and choosing suitable parameters for an application. In this tutorial, we outline methodologies to solve these issues and allow for conversion of any application to the encrypted domain using both leveled and fully homomorphic encryption. The first approach, called...

Differential privacy is a fundamental concept for protecting individual privacy in databases while enabling data analysis. Conceptually, it is assumed that the adversary has no direct access to the database, and therefore, encryption is not necessary. However, with the emergence of cloud computing and the «on-cloud» storage of vast databases potentially contributed by multiple parties, it is becoming increasingly necessary to consider the possibility of the adversary having (at least...

We study a fundamental problem in Multi-Party Computation, which we call the Multiple Millionaires’ Problem (MMP). Given a set of private integer inputs, the problem is to identify the subset of inputs that equal the maximum (or minimum) of that set, without revealing any further information on the inputs beyond what is implied by the desired output. Such a problem is a natural extension of the Millionaires’ Problem, which is the very first Multi- Party Computation problem that was...

The privacy-preserving machine learning (PPML) has gained growing importance over the last few years. One of the biggest challenges is to improve the efficiency of PPML so that the communication and computation costs of PPML are affordable for large machine learning models such as deep learning. As we know, linear algebra such as matrix multiplication occupies a significant part of the computation in deep learning such as deep convolutional neural networks (CNN). Thus, it is desirable to...

Explainable AI (XAI) refers to the development of AI systems and machine learning models in a way that humans can understand, interpret and trust the predictions, decisions and outputs of these models. A common approach to explainability is feature importance, that is, determining which input features of the model have the most significant impact on the model prediction. Two major techniques for computing feature importance are LIME (Local Interpretable Model-agnostic Explanations) and...

Computing the distance between two non-normalized vectors $\mathbfit{x}$ and $\mathbfit{y}$, represented by $\Delta(\mathbfit{x},\mathbfit{y})$ and comparing it to a predefined public threshold $\tau$ is an essential functionality used in privacy-sensitive applications such as biometric authentication, identification, machine learning algorithms ({\em e.g.,} linear regression, k-nearest neighbors, etc.), and typo-tolerant password-based authentication. Tackling a widely used distance...

Amid the landscape of confidential computing, where security and privacy reign supreme, PRIVATON emerges as a pioneering and practical solution to safeguard sensitive data and computations. A verifiable proof of computation model, with one of its variant built upon the dual sandbox strategy, PRIVATON combines Trusted Execution Environment (TEE) technologies with WebAssembly (WASM) runtime environments to establish an ecosystem for privacy-preserving computations. This approach involves fine...

As cloud computing continues to gain widespread adoption, safeguarding the confidentiality of data entrusted to third-party cloud service providers becomes a critical concern. While traditional encryption methods offer protection for data at rest and in transit, they fall short when it comes to where it matters the most, i.e., during data processing. To address this limitation, we present HELM, a framework for privacy-preserving data processing using homomorphic encryption. HELM...

The privacy concern in smart contract applications continues to grow, leading to the proposal of various schemes aimed at developing comprehensive and universally applicable privacy-preserving smart contract (PPSC) schemes. However, the existing research in this area is fragmented and lacks a comprehensive system overview. This paper aims to bridge the existing research gap on PPSC schemes by systematizing previous studies in this field. The primary focus is on two categories: PPSC schemes...

Real-world healthcare data sharing is instrumental in constructing broader-based and larger clinical data sets that may improve clinical decision-making research and outcomes. Stakeholders are frequently reluctant to share their data without guaranteed patient privacy, proper protection of their data sets, and control over the usage of their data. Fully homomorphic encryption (FHE) is a cryptographic capability that can address these issues by enabling computation on encrypted data without...

In today’s interconnected world, data has become a valuable asset, leading to a growing interest in protecting it through techniques such as privacy-preserving computation. Two well-known approaches are multi-party computation and homomorphic encryption with use cases such as privacy-preserving machine learning evaluating or training neural networks. For multi-party computation, one of the fundamental arithmetic operations is the secure multiplication in the malicious security model and by...

Inventory matching is a standard mechanism for trading financial stocks by which buyers and sellers can be paired. In the financial world, banks often undertake the task of finding such matches between their clients. The related stocks can be traded without adversely impacting the market price for either client. If matches between clients are found, the bank can offer the trade at advantageous rates. If no match is found, the parties have to buy or sell the stock in the public market, which...

Partially Oblivious Pseudorandom Functions (POPRFs) are 2-party protocols that allow a client to learn pseudorandom function (PRF) evaluations on inputs of its choice from a server. The client submits two inputs, one public and one private. The security properties ensure that the server cannot learn the private input, and the client cannot learn more than one evaluation per POPRF query. POPRFs have many applications including password-based key exchange and privacy-preserving authentication...

Privacy-preserving transaction systems on blockchain networks like Monero or Zcash provide complete transaction anonymity through cryptographic commitments or encryption. While this secures privacy, it inhibits the collection of statistical data, which current financial markets heavily rely on for economic and sociological research conducted by central banks, statistics bureaus, and research companies. Differential privacy techniques have been proposed to preserve individuals' privacy while...

Privacy-preserving neural network based on secure multi-party computation (MPC) enables multiple parties to jointly train neural network models without revealing sensitive data. In privacy-preserving neural network, the high communication costs of securely computing non-linear functions is the primary performance bottleneck. For commonly used non-linear functions, such as ReLU, existing work adopts an offline-online computation paradigm and utilizes distributed comparison function (DCF) to...

In practical operational networks, it is essential to validate path integrity, especially when untrusted intermediate nodes are from numerous network infrastructures operated by several network authorities. Current solutions often reveal the entire path to all parties involved, which may potentially expose the network structures to malicious intermediate attackers. Additionally, there is no prior work done to provide a systematic approach combining the complete lifecycle of packet delivery,...

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...

Threshold Fully Homomorphic Encryption (ThFHE) enables arbitrary computation over encrypted data while keeping the decryption key distributed across multiple parties at all times. ThFHE is a key enabler for threshold cryptography and, more generally, secure distributed computing. Existing ThFHE schemes relying on standard hardness assumptions, inherently require highly inefficient parameters and are unsuitable for practical deployment. In this paper, we take a novel approach towards making...

Data privacy concerns are increasing significantly in the context of Internet of Things, cloud services, edge computing, artificial intelligence applications, and other applications enabled by next generation networks. Homomorphic Encryption addresses privacy challenges by enabling multiple operations to be performed on encrypted messages without decryption. This paper comprehensively addresses homomorphic encryption from both theoretical and practical perspectives. The paper delves into the...

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...

We introduce and investigate the privacy-preserving version of collaborative data cleaning. With collaborative data cleaning, two parties want to reconcile their data sets to filter out badly classified, misclassified data items. In the privacy-preserving (private) version of data cleaning, the additional security goal is that parties should only learn their misclassified data items, but nothing else about the other party's data set. The problem of private data cleaning is essentially a...

The overhead of non-linear functions dominates the performance of the secure multiparty computation (MPC) based privacy-preserving machine learning (PPML). This work introduces a family of novel secure three-party computation (3PC) protocols, Bicoptor, which improve the efficiency of evaluating non-linear functions. The basis of Bicopter is a new sign determination protocol, which relies on a clever use of the truncation protocol proposed in SecureML (S\&P 2017). Our 3PC sign...

The Massive Parallel Computing (MPC) model gained wide adoption over the last decade. By now, it is widely accepted as the right model for capturing the commonly used programming paradigms (such as MapReduce, Hadoop, and Spark) that utilize parallel computation power to manipulate and analyze huge amounts of data. Motivated by the need to perform large-scale data analytics in a privacy-preserving manner, several recent works have presented generic compilers that transform algorithms in...

The offline-online model is a leading paradigm for practical secure multi-party computation (MPC) protocol design that has successfully reduced the overhead for several prevalent privacy-preserving computation functionalities common to diverse application domains. However, the prohibitive overheads associated with secure comparison -- one of these vital functionalities -- often bottlenecks current and envisioned MPC solutions. Indeed, an efficient secure comparison solution has the potential...

The massive scale and performance demands of privacy-preserving data aggregation make integration of security and privacy difficult. Traditional tools in private computing are not well-suited to handle these challenges, especially for more limited client devices. Efficient primitives and protocols for secure and private data aggregation are a promising approach for private data analytics with resource-constrained devices. However, even such efficient primitives may be much slower than...

As cloud computing becomes increasingly ubiquitous, protecting the confidentiality of data outsourced to third parties becomes a priority. While encryption is a natural solution to this problem, traditional algorithms may only protect data at rest and in transit, but do not support encrypted processing. In this work we introduce Romeo, which enables easy-to-use privacy-preserving processing of data in the cloud using homomorphic encryption. Romeo automatically converts arbitrary programs...

We propose a novel obfuscation technique that can be used to outsource hard satisfiability (SAT) formulas to the cloud. Servers with large computational power are typically used to solve SAT instances that model real-life problems in task scheduling, AI planning, circuit verification and more. However, outsourcing data to the cloud may lead to privacy and information breaches since satisfying assignments may reveal considerable information about the underlying problem modeled by SAT. In...

Homomorphic encryption is a powerful privacy-preserving technology that is notoriously difficult to configure, even for experts. In this article, we outline methodologies for determining optimal cryptographic parameters for any arbitrary application. We provide guidelines for both leveled and fully homomorphic encryption, and demonstrate the presented strategies with the BGV cryptosystem.

Digital signature is an essential primitive in cryptography, which can be used as the digital analogue of handwritten signatures but also as a building block for more complex systems. In the latter case, signatures with specific features are needed, so as to smoothly interact with the other components of the systems, such as zero-knowledge proofs. This has given rise to so-called signatures with efficient protocols, a versatile tool that has been used in countless applications. Designing...

Homomorphic encryption enables computation on encrypted data, and hence it has a great potential in privacy-preserving outsourcing of computations to the cloud. Hardware acceleration of homomorphic encryption is crucial as software implementations are very slow. In this paper, we present design methodologies for building a programmable hardware accelerator for speeding up the cloud-side homomorphic evaluations on encrypted data. First, we propose a divide-and-conquer technique that...

In recent years, lattice-based secure multi-party computation (MPC) has seen a rise in popularity and is used more and more in large scale applications like privacy-preserving cloud computing, electronic voting, or auctions. Many of these applications come with the following high security requirements: a computation result should be publicly verifiable, with everyone being able to identify a malicious party and hold it accountable, and a malicious party should not be able to corrupt the...

Fully homomorphic encryption (FHE) provides a natural solution for privacy-preserving cloud computing, but a straightforward FHE protocol may suffer from high computational overhead and a large ciphertext expansion rate, especially for computation-intensive tasks over large data, which are the main obstacles toward practical privacy-preserving cloud computing. In this paper, we present HEAD, a generic privacy-preserving cloud computing protocol that can be based on most mainstream (typically...

We address the problem of learning a machine learning model from training data that originates at multiple data owners, while providing formal privacy guarantees regarding the protection of each owner's data. Existing solutions based on Differential Privacy (DP) achieve this at the cost of a drop in accuracy. Solutions based on Secure Multiparty Computation (MPC) do not incur such accuracy loss but leak information when the trained model is made publicly available. We propose an MPC solution...

Payment Channel Networks or PCNs solve the problem of scalability in Blockchain by executing payments off-chain. Due to a lack of sufficient capacity in the network, high-valued payments are split and routed via multiple paths. Existing multi-path payment protocols either fail to achieve atomicity or are susceptible to wormhole attack. We propose a secure and privacy-preserving atomic multi-path payment protocol CryptoMaze. Our protocol avoids the formation of multiple off-chain contracts on...

Recent works challenged the Number-Theoretic Transform (NTT) as the most efficient method for polynomial multiplication in GPU implementations of Fully Homomorphic Encryption schemes such as CKKS and BFV. In particular, these works argue that the Discrete Galois Transform (DGT) is a better candidate for this particular case. However, these claims were never rigorously validated, and only intuition was used to argue in favor of each transform. This work brings some light on the dis- cussion...

Fully homomorphic encryption enables computation on encrypted data, and hence it has a great potential in privacy-preserving outsourcing of computations. In this paper, we present a complete instruction-set processor architecture ‘Medha’ for accelerating the cloud-side operations of an RNS variant of the HEAAN homomorphic encryption scheme. Medha has been designed following a modular hardware design approach to attain a fast computation time for computationally expensive homomorphic...

Zero-Knowledge Proofs (ZKPs) are cryptographic primitives allowing a party to prove to another party that the former knows some information while keeping it secret. Such a premise can lead to the development of numerous privacy-preserving protocols in different scenarios, like proving knowledge of some credentials to a server without leaking the identity of the user. Even when the applications of ZKPs were endless, they were not exploited in the wild for a couple of decades due to the fact...

In crowd-sourced data aggregation, participants share their data points with curators. However, the lack of privacy guarantees may discourage participation, which motivates the need for privacy-preserving aggregation protocols. Unfortunately, existing solutions do not support public auditing without revealing the participants' data. In real-world applications, there is a need for public verifiability (i.e., verifying the protocol correctness) while preserving the privacy of the participants'...

Recently, the application of multi-party secure computing schemes based on homomorphic encryption in the field of machine learning attracts attentions across the research fields. Previous studies have demonstrated that secure protocols adopting packed additive homomorphic encryption (PAHE) schemes based on the ring learning with errors (RLWE) problem exhibit significant practical merits, and are particularly promising in enabling efficient secure inference in machine-learning-as-a-service...

BitBadges is a privacy preserving distributed identity platform that plans on utilizing CouchDB, the decentralized-internet SDK by Lonero, Blake3 hashing, and a PoCP or Proof of Computation consensus algorithm. It is privacy-preserving and offers a unique proposition for traditional blockchains centered around consensus algorithms. This paper introduces the conceptual design for BitBadges in its second version and as its own blockchain platform and cryptocurrency. The aim is to introduce...

With the rapid development of cloud computing, an increasing number of companies are adopting cloud storage technology to reduce overhead. However, to ensure the privacy of sensitive data, the uploaded data need to be encrypted before being outsourced to the cloud. The concept of public-key encryption with keyword search (PEKS) was introduced by Boneh \textit{et al.} to provide flexible usage of the encrypted data. Unfortunately, most of the PEKS schemes are not secure against inside...

While the practicality of secure multi-party computation (MPC) has been extensively analyzed and improved over the past decade, we are hitting the limits of efficiency with the traditional approaches of representing the computed functionalities as generic arithmetic or Boolean circuits. This work follows the design principle of identifying and constructing fast and provably-secure MPC protocols to evaluate useful high-level algebraic abstractions; thus, improving the efficiency of all...

In more detail, we propose Soteria, a system for distributed privacy-preserving ML that leverages Trusted Execution Environments (e.g., Intel SGX) to run computations over sensitive information in isolated containers (enclaves). Unlike previous work, where all ML-related computation is performed at trusted enclaves, we introduce a hybrid scheme, combining computation done inside and outside these enclaves. The experimental evaluation validates that our approach reduces the runtime of ML...

Secure Multiparty Computation (MPC) is an invaluable tool for training machine learning models when the training data cannot be directly accessed by the model trainer. Unfortunately, complex algorithms, such as deep learning models, have their computational complexities increased by orders of magnitude when performed using MPC protocols. In this contribution, we study how to efficiently train an important class of machine learning problems by using MPC where features are known by one of the...

Merkle tree is applied in diverse applications, namely, Blockchain, smart grid, IoT, Biomedical, financial transactions, etc., to verify authenticity and integrity. Also, the Merkle tree is used in privacy-preserving computing. However, the Merkle tree is a computationally costly data structure. It uses cryptographic string hash functions to partially verify the data integrity and authenticity of a data block. However, the verification process creates unnecessary network traffic because it...

In the era of cloud computing and machine learning, data has become a highly valuable resource. Recent history has shown that the benefits brought forth by this data driven culture come at a cost of potential data leakage. Such breaches have a devastating impact on individuals and industry, and lead the community to seek privacy preserving solutions. A promising approach is to utilize Fully Homomorphic Encryption (FHE) to enable machine learning over encrypted data, thus providing resiliency...

Privacy is a huge concern for cryptocurrencies and blockchains as most of these systems log everything in the clear. This has resulted in several academic and industrial initiatives to address privacy. Starting with the UTXO model of Bitcoin, initial works brought confidentiality and anonymity to payments. Recent works have expanded to support more generalized forms of private computation. Such solutions tend to be highly involved as they rely on advanced cryptographic primitives and...

We introduce CryptGPU, a system for privacy-preserving machine learning that implements all operations on the GPU (graphics processing unit). Just as GPUs played a pivotal role in the success of modern deep learning, they are also essential for realizing scalable privacy-preserving deep learning. In this work, we start by introducing a new interface to losslessly embed cryptographic operations over secret-shared values (in a discrete domain) into floating-point operations that can be...

Edge computing and caching have emerged as key technologies in the future communication network to enhance the user experience, reduce backhaul traffic, and enable various Internet of Things applications. Different from conventional resources like CPU and memory that can be utilized by only one party at a time, a cached data item, which can be considered as a public good, can serve multiple parties simultaneously. Therefore, instead of independent caching, it is beneficial for the parties...

This research revisits the fundamental problem of processing privacy-preserving Boolean queries over outsourced databases on untrusted public clouds. Many current Searchable Encryption (SE) schemes try to seek an appropriate trade-off between security and efficiency, yet most of them suffer from an unacceptable query leakage due to their conjunctive/disjunctive terms that are processed individually. We show, however, this trade-off still can be deeply optimized for more security. We consider...

Many video classification applications require access to personal data, thereby posing an invasive security risk to the users' privacy. We propose a privacy-preserving implementation of single-frame method based video classification with convolutional neural networks that allows a party to infer a label from a video without necessitating the video owner to disclose their video to other entities in an unencrypted manner. Similarly, our approach removes the requirement of the classifier owner...

Lattice-based cryptography forms the mathematical basis for homomorphic encryption, which allows computation directly on encrypted data. Homomorphic encryption enables privacy-preserving applications such as secure cloud computing; yet, its practical applications suffer from the high computational complexity of homomorphic operations. Fast implementations of the homomorphic encryption schemes heavily depend on efficient polynomial arithmetic; multiplication of very large degree polynomials...

Binarized Neural Networks (BNN) provide efficient implementations of Convolutional Neural Networks (CNN). This makes them particularly suitable to perform fast and memory-light inference of neural networks running on resource-constrained devices. Motivated by the growing interest in CNN-based biometric recognition on potentially insecure devices, or as part of strong multi-factor authentication for sensitive applications, the protection of BNN inference on edge devices is rendered...

Nowadays, information is known as the main asset of each organization, which causes data generation to be exponentially increasing. Hence, different capacity issues and requirements show up with it, e.g. storage and maintenance of generating data, searching among them, and analyzing them. Cloud computing is one of the common technologies used to meet these requirements. Popularity of this technology is extremely growing as it can be used to handle high amount of data in a cost efficient and...

The Cheon-Kim-Kim-Song (CKKS) scheme (Asiacrypt'17) is one of the most promising homomorphic encryption (HE) schemes as it enables privacy-preserving computing over real (or complex) numbers. It is known that bootstrapping is the most challenging part of the CKKS scheme. Further, homomorphic evaluation of modular reduction is the core of the CKKS bootstrapping, but as modular reduction is not represented by the addition and multiplication of complex numbers, approximate polynomials for...

Cloud computing and cloud storage are among the most efficient technologies for storing and processing metadata. But there are many privacy concerns within this domain. Most of the challenges are coming from trusted or semi trusted cloud servers where some computations must be applied to high confidential data. Data encryption can solve some confidentiality issues on the cloud but it is not easy to provide privacy preserving data processing services such as searching a query over encrypted...

Benchmarking the performance of companies is essential to identify improvement potentials in various industries. Due to a competitive environment, this process imposes strong privacy needs, as leaked business secrets can have devastating effects on participating companies. Consequently, related work proposes to protect sensitive input data of companies using secure multi-party computation or homomorphic encryption. However, related work so far does not consider that also the benchmarking...

With the emerging popularity of cloud computing, the problem of how to query over cryptographically-protected data has been widely studied. However, most existing works focus on querying protected relational databases, few work has shown interests in graph databases. In this paper, we first investigate and summarize two single-instruction queries, namely Graph Pattern Matching (GPM) and Graph Navigation (GN). Then we follow their design intuitions and leverage secure Multi-Party Computation...

Conflict-free Replicated Data Types (CRDTs) are abstract data types that support developers when designing and reasoning about distributed systems with eventual consistency guarantees. In their core they solve the problem of how to deal with concurrent operations, in a way that is transparent for developers. However in the real world, distributed systems also suffer from other relevant problems, including security and privacy issues and especially when participants can be untrusted. In this...

Secure delegated quantum computing is a two-party cryptographic primitive, where a computationally weak client wishes to delegate an arbitrary quantum computation to an untrusted quantum server in a privacy-preserving manner. Communication via quantum channels is typically assumed such that the client can establish the necessary correlations with the server to securely perform the given task. This has the downside that all these protocols cannot be put to work for the average user unless a...

A cryptographic primitive, called encryption switching protocol (ESP), has been proposed recently. This two-party protocol enables interactively converting values encrypted under one scheme into another scheme without revealing the plaintexts. Given two additively and multiplicatively homomorphic encryption schemes, parties can now encrypt their data and convert underlying encryption schemes to perform different operations simultaneously. Due to its efficiency, ESP becomes an alternative to...

Mass surveillance can be more easily achieved leveraging fear and desire of the population to feel protected while affected by devastating events. Indeed, in such scenarios, governments can adopt exceptional measures that limit civil rights, usually receiving large support from citizens. The COVID-19 pandemic is currently affecting daily life of many citizens in the world. People are forced to stay home for several weeks, unemployment rates quickly increase, uncertainty and sadness...

The advances in machine learning have revealed its great potential for emerging mobile applications such as face recognition and voice assistant. Models trained via a Neural Network (NN) can offer accurate and efficient inference services for mobile users. Unfortunately, the current deployment of such service encounters privacy concerns. Directly offloading the model to the mobile device violates model privacy of the model owner, while feeding user input to the service compromises user...

Computing on data in a manner that preserve the privacy is of growing importance. Multi-Party Computation (MPC) and Homomorphic Encryption (HE) are two cryptographic techniques for privacy-preserving computations. In this work, we have developed efficient UC-secure multiparty protocols for matrix multiplications and two-dimensional convolutions. We built upon the SPDZ framework and integrated the state-of-the-art HE algorithms for matrix multiplication. Our protocol achieved communication...

We study the problem of secure two-party computation of arithmetic circuits in the presence of active (``malicious'') parties. This problem is motivated by privacy-preserving numerical computations, such as ones arising in the context of machine learning training and classification, as well as in threshold cryptographic schemes. In this work, we design, optimize, and implement an actively secure protocol for secure two-party arithmetic computation. A distinctive feature of our protocol is...

Massively Parallel Computation (MPC) is a model of computation widely believed to best capture realistic parallel computing architectures such as large-scale MapReduce and Hadoop clusters. Motivated by the fact that many data analytics tasks performed on these platforms involve sensitive user data, we initiate the theoretical exploration of how to leverage MPC architectures to enable efficient, privacy-preserving computation over massive data. Clearly if a computation task does not lend...

Symmetric Searchable Encryption (SSE) allows the outsourcing of encrypted data to possible untrusted third party services while simultaneously giving the opportunity to users to search over the encrypted data in a secure and privacy-preserving way. Currently, the majority of SSE schemes have been designed to fit a typical cloud service scenario where users (clients) encrypt their data locally and upload them securely to a remote location. While this scenario fits squarely the cloud paradigm,...

Efficiently supporting inference tasks of deep neural network (DNN) on the resource-constrained Internet of Things (IoT) devices has been an outstanding challenge for emerging smart systems. To mitigate the burden on IoT devices, one prevalent solution is to outsource DNN inference tasks to the public cloud. However, this type of ``cloud-backed" solutions can cause privacy breach since the outsourced data may contain sensitive information. For privacy protection, the research community has...

Machine learning tools have illustrated their potential in many significant sectors such as healthcare and finance, to aide in deriving useful inferences. The sensitive and confidential nature of the data, in such sectors, raises natural concerns for the privacy of data. This motivated the area of Privacy-preserving Machine Learning (PPML) where privacy of the data is guaranteed. Typically, ML techniques require large computing power, which leads clients with limited infrastructure to rely...

With the proliferation of data and emerging data-driven applications, how to perform data analytical operations while respecting privacy concerns has become a very interesting research topic. With the advancement of communication and computing technologies, e.g. the FoG computing concept and its associated Edge computing technologies, it is now appealing to deploy decentralized data-driven applications. Following this trend, in this paper, we investigate privacy-preserving singular value...

Developing machine learning models from federated training data, containing many independent samples, is an important task that can significantly enhance the potential applicability and prediction power of learned models. Since single users, like hospitals or individual labs, typically collect data-sets that do not support accurate learning with high confidence, it is desirable to combine data from several users without compromising data privacy. In this paper, we develop a...

Blind signatures constitute basic cryptographic ingredients for privacy-preserving applications such as anonymous credentials, e-voting, and Bitcoin. Despite the great variety of cryptographic applications blind signatures also found their way in real-world scenarios. Due to the expected progress in cryptanalysis using quantum computers, it remains an important research question to find practical and secure alternatives to current systems based on the hardness of classical security...

Clustering is a common technique for data analysis, which aims to partition data into similar groups. When the data comes from different sources, it is highly desirable to maintain the privacy of each database. In this work, we study a popular clustering algorithm (K-means) and adapt it to the privacy-preserving context. Specifically, to construct our privacy-preserving clustering algorithm, we first propose an efficient batched Euclidean squared distance computation protocol in the...

With the rapid increase in cloud computing, concerns surrounding data privacy, security, and confidentiality also have been increased significantly. Not only cloud providers are susceptible to internal and external hacks, but also in some scenarios, data owners cannot outsource the computation due to privacy laws such as GDPR, HIPAA, or CCPA. Fully Homomorphic Encryption (FHE) is a groundbreaking invention in cryptography that, unlike traditional cryptosystems, enables computation on...

In the area of cloud computing, judging the fulfillment of service-level agreements on a technical level is gaining more and more importance. To support this we introduce privacy preserving set relations as inclusiveness and disjointness based on Bloom filters. We propose to compose them in a slightly different way by applying a keyed hash function. Besides discussing the correctness of the set relations, we analyze how this impacts the privacy of the sets content as well as providing...

Secure multi-party computation (MPC) allows multiple parties to jointly compute the output of a function while preserving the privacy of any individual party's inputs to that function. As MPC protocols transition from research prototypes to real-world applications, the usability of MPC-enabled applications is increasingly critical to their successful deployment and wide adoption. Our Web-MPC platform, designed with a focus on usability, has been deployed for privacy-preserving data...