The Theory and Application of Privacy-preserving Computation

Privacy is a growing concern in the digital world as more information becomes digital every day. Often the implications of how this information could be exploited for nefarious purposes are not explored until after the fact. The public is becoming more concerned about this. This dissertation introduces a new paradigm for tackling the problem, namely, transferable multiparty computation (T-MPC). T-MPC builds upon existing multiparty computation work yet allows some additional flexibility in the set of participants. T-MPC is orders of magnitude more efficient for certain applications. This greatly increases the scalability of the sizes of networks supported for privacy-preserving computation

Dynamic Secrets and Secret Keys Based Scheme for Securing Last Mile Smart Grid Wireless Communication

An integrated and optimized smart grid cannot be achieved without a secure communication network. Due to the large-scale nature of the power system, the variety of technologies used, and limitations of communication bandwidth, supervisory applications for smart grid still use weak security in many deployments. Adversaries can potentially modify measurement values or inject bad commands over the network. In this paper, we propose a novel scheme based on dynamic secrets and encryption with secret keys. The scheme generates a series of dynamic secrets over the communication network, which are used to generate secret keys for data encryption. The generation of dynamic secret is frequent and no adversary can compromise the network for a longer period, even if he/she knows a secret key. The scheme is secure against eavesdropping, malicious communication injection, man-in-the-middle attack, replay attack, impersonation attack, and chosen-plaintext attack. The security analysis and performance evaluation show that our scheme is feasible to be used in the communication between supervisory and control nodes of various smart grid applications

MPCFormer: fast, performant and private Transformer inference with MPC

Enabling private inference is crucial for many cloud inference services that are based on Transformer models. However, existing private inference solutions for Transformers can increase the inference latency by more than 60x or significantly compromise the quality of inference results. In this paper, we design the framework MPCFORMER using secure multi-party computation (MPC) and Knowledge Distillation (KD). It can be used in tandem with many specifically designed MPC-friendly approximations and trained Transformer models. MPCFORMER significantly speeds up Transformer model inference in MPC settings while achieving similar ML performance to the input model. We evaluate MPCFORMER with various settings in MPC. On the IMDb dataset, we achieve similar performance to BERTBASE, while being 5.3x faster. On the GLUE benchmark, we achieve 97% performance of BERTBASE with a 2.2x speedup. We show that MPCFORMER remains effective with different trained Transformer weights such as ROBERTABASE and larger models including BERTLarge. In particular, we achieve similar performance to BERTLARGE, while being 5.93x faster on the IMDb dataset

Controlled secure social cloud data sharing based on a novel identity based proxy re-encryption plus scheme

Currently we are witnessing a rapid integration of social networks and cloud computing, especially on storing social media contents on cloud storage due to its cheap management and easy accessing at any time and from any place. However, how to securely store and share social media contents such as pictures/videos among social groups is still a very challenging problem. In this paper, we try to tackle this problem by using a new cryptographic primitive: identity based proxy re-encryption plus (IBPRE ), which is a variant of proxy re-encryption (PRE). In PRE, by using re-encryption keys, a ciphertext computed for Alice can be transferred to a new one for Bob. Recently, the concept of PRE plus (PRE) was introduced by Wang et al. In PRE, all the algorithms are almost the same as traditional PRE, except the re-encryption keys are generated by the encrypter instead of the delegator. The message-level based fine-grained delegation property and the weak non-transferable property can be easily achieved by PRE , while traditional PRE cannot achieve them. Based on the 3-linear map, we first propose a new IBE scheme and a new IBPRE scheme, we prove the security of these schemes and give the properties and performance analysis of the new IBPRE scheme. Finally, we propose a new framework based on this new primitive for secure cloud social data sharingPeer ReviewedPostprint (author's final draft

SoK: Privacy-Enhancing Technologies in Finance

Recent years have seen the emergence of practical advanced cryptographic tools that not only protect data privacy and authenticity, but also allow for jointly processing data from different institutions without sacrificing privacy. The ability to do so has enabled implementations a number of traditional and decentralized financial applications that would have required sacrificing privacy or trusting a third party. The main catalyst of this revolution was the advent of decentralized cryptocurrencies that use public ledgers to register financial transactions, which must be verifiable by any third party, while keeping sensitive data private. Zero Knowledge (ZK) proofs rose to prominence as a solution to this challenge, allowing for the owner of sensitive data (e.g. the identities of users involved in an operation) to convince a third party verifier that a certain operation has been correctly executed without revealing said data. It quickly became clear that performing arbitrary computation on private data from multiple sources by means of secure Multiparty Computation (MPC) and related techniques allows for more powerful financial applications, also in traditional finance. In this SoK, we categorize the main traditional and decentralized financial applications that can benefit from state-of-the-art Privacy-Enhancing Technologies (PETs) and identify design patterns commonly used when applying PETs in the context of these applications. In particular, we consider the following classes of applications: 1. Identity Management, KYC & AML; and 2. Markets & Settlement; 3. Legal; and 4. Digital Asset Custody. We examine how ZK proofs, MPC and related PETs have been used to tackle the main security challenges in each of these applications. Moreover, we provide an assessment of the technological readiness of each PET in the context of different financial applications according to the availability of: theoretical feasibility results, preliminary benchmarks (in scientific papers) or benchmarks achieving real-world performance (in commercially deployed solutions). Finally, we propose future applications of PETs as Fintech solutions to currently unsolved issues. While we systematize financial applications of PETs at large, we focus mainly on those applications that require privacy preserving computation on data from multiple parties

Analysis of a consensus protocol for extending consistent subchains on the bitcoin blockchain

Currently, an increasing number of third-party applications exploit the Bitcoin blockchain to store tamper-proof records of their executions, immutably. For this purpose, they leverage the few extra bytes available for encoding custom metadata in Bitcoin transactions. A sequence of records of the same application can thus be abstracted as a stand-alone subchain inside the Bitcoin blockchain. However, several existing approaches do not make any assumptions about the consistency of their subchains, either (i) neglecting the possibility that this sequence of messages can be altered, mainly due to unhandled concurrency, network malfunctions, application bugs, or malicious users, or (ii) giving weak guarantees about their security. To tackle this issue, in this paper, we propose an improved version of a consensus protocol formalized in our previous work, built on top of the Bitcoin protocol, to incentivize third-party nodes to consistently extend their subchains. Besides, we perform an extensive analysis of this protocol, both defining its properties and presenting some real-world attack scenarios, to show how its specific design choices and parameter configurations can be crucial to prevent malicious practices

EPIC: Efficient Private Image Classification (or: Learning from the Masters)

Outsourcing an image classification task raises privacy concerns, both from the image provider\u27s perspective, who wishes to keep their images confidential, and from the classification algorithm provider\u27s perspective, who wishes to protect the intellectual property of their classifier. We propose EPIC, an efficient private image classification system based on support vector machine (SVM) learning, which is secure against malicious adversaries. The novelty of EPIC is that it builds upon transfer learning techniques known from the Machine Learning (ML) literature and minimizes the load on the privacy-preserving part. Our solution is based on Secure Multiparty Computation (MPC), it is 34 times faster than Gazelle (USENIX 2018) --the state-of-the-art in private image classification-- and it improves the total communication cost by 50 times, while achieving a 7\% higher accuracy on CIFAR-10 dataset. When benchmarked for performance, while maintaining the same CIFAR-10 accuracy as Gazelle, EPIC is 700 times faster and the communication cost is reduced by 500 times

State of the art in privacy preservation in video data

Active and Assisted Living (AAL) technologies and services are a possible solution to address the crucial challenges regarding health and social care resulting from demographic changes and current economic conditions. AAL systems aim to improve quality of life and support independent and healthy living of older and frail people. AAL monitoring systems are composed of networks of sensors (worn by the users or embedded in their environment) processing elements and actuators that analyse the environment and its occupants to extract knowledge and to detect events, such as anomalous behaviours, launch alarms to tele-care centres, or support activities of daily living, among others. Therefore, innovation in AAL can address healthcare and social demands while generating economic opportunities. Recently, there has been far-reaching advancements in the development of video-based devices with improved processing capabilities, heightened quality, wireless data transfer, and increased interoperability with Internet of Things (IoT) devices. Computer vision gives the possibility to monitor an environment and report on visual information, which is commonly the most straightforward and human-like way of describing an event, a person, an object, interactions and actions. Therefore, cameras can offer more intelligent solutions for AAL but they may be considered intrusive by some end users. The General Data Protection Regulation (GDPR) establishes the obligation for technologies to meet the principles of data protection by design and by default. More specifically, Article 25 of the GDPR requires that organizations must "implement appropriate technical and organizational measures [...] which are designed to implement data protection principles [...] , in an effective manner and to integrate the necessary safeguards into [data] processing.” Thus, AAL solutions must consider privacy-by-design methodologies in order to protect the fundamental rights of those being monitored. Different methods have been proposed in the latest years to preserve visual privacy for identity protection. However, in many AAL applications, where mostly only one person would be present (e.g. an older person living alone), user identification might not be an issue; concerns are more related to the disclosure of appearance (e.g. if the person is dressed/naked) and behaviour, what we called bodily privacy. Visual obfuscation techniques, such as image filters, facial de-identification, body abstraction, and gait anonymization, can be employed to protect privacy and agreed upon by the users ensuring they feel comfortable. Moreover, it is difficult to ensure a high level of security and privacy during the transmission of video data. If data is transmitted over several network domains using different transmission technologies and protocols, and finally processed at a remote location and stored on a server in a data center, it becomes demanding to implement and guarantee the highest level of protection over the entire transmission and storage system and for the whole lifetime of the data. The development of video technologies, increase in data rates and processing speeds, wide use of the Internet and cloud computing as well as highly efficient video compression methods have made video encryption even more challenging. Consequently, efficient and robust encryption of multimedia data together with using efficient compression methods are important prerequisites in achieving secure and efficient video transmission and storage.This publication is based upon work from COST Action GoodBrother - Network on Privacy-Aware Audio- and Video-Based Applications for Active and Assisted Living (CA19121), supported by COST (European Cooperation in Science and Technology). COST (European Cooperation in Science and Technology) is a funding agency for research and innovation networks. Our Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation. www.cost.e