Privacy-Preserving Multi-Party Reconciliation Secure in the Malicious Model (Extended version)

The problem of fair and privacy-preserving ordered set reconciliation arises in a variety of applications like auctions, e-voting, and appointment reconciliation. While several multi-party protocols have been proposed that solve this problem in the semi-honest model, there are no multi-party protocols that are secure in the malicious model so far. In this paper, we close this gap. Our newly proposed protocols are shown to be secure in the malicious model based on a variety of novel non-interactive zero-knowledge-proofs. We describe the implementation of our protocols and evaluate their performance in comparison to protocols solving the problem in the semi-honest case

On the Commitment Capacity of Unfair Noisy Channels

Noisy channels are a valuable resource from a cryptographic point of view. They can be used for exchanging secret-keys as well as realizing other cryptographic primitives such as commitment and oblivious transfer. To be really useful, noisy channels have to be consider in the scenario where a cheating party has some degree of control over the channel characteristics. Damg\r{a}rd et al. (EUROCRYPT 1999) proposed a more realistic model where such level of control is permitted to an adversary, the so called unfair noisy channels, and proved that they can be used to obtain commitment and oblivious transfer protocols. Given that noisy channels are a precious resource for cryptographic purposes, one important question is determining the optimal rate in which they can be used. The commitment capacity has already been determined for the cases of discrete memoryless channels and Gaussian channels. In this work we address the problem of determining the commitment capacity of unfair noisy channels. We compute a single-letter characterization of the commitment capacity of unfair noisy channels. In the case where an adversary has no control over the channel (the fair case) our capacity reduces to the well-known capacity of a discrete memoryless binary symmetric channel

Multi-factor Physical Layer Security Authentication in Short Blocklength Communication

Lightweight and low latency security schemes at the physical layer that have recently attracted a lot of attention include: (i) physical unclonable functions (PUFs), (ii) localization based authentication, and, (iii) secret key generation (SKG) from wireless fading coefficients. In this paper, we focus on short blocklengths and propose a fast, privacy preserving, multi-factor authentication protocol that uniquely combines PUFs, proximity estimation and SKG. We focus on delay constrained applications and demonstrate the performance of the SKG scheme in the short blocklength by providing a numerical comparison of three families of channel codes, including half rate low density parity check codes (LDPC), Bose Chaudhuri Hocquenghem (BCH), and, Polar Slepian Wolf codes for n=512, 1024. The SKG keys are incorporated in a zero-round-trip-time resumption protocol for fast re-authentication. All schemes of the proposed mutual authentication protocol are shown to be secure through formal proofs using Burrows, Abadi and Needham (BAN) and Mao and Boyd (MB) logic as well as the Tamarin-prover

Counterfactual quantum certificate authorization

We present a multi-partite protocol in a counterfactual paradigm. In counterfactual quantum cryptography, secure information is transmitted between two spatially separated parties even when there is no physical travel of particles transferring the information between them. We propose here a tripartite counterfactual quantum protocol for the task of certificate authorization. Here a trusted third party, Alice, authenticates an entity Bob (e.g., a bank) that a client Charlie wishes to securely transact with. The protocol is counterfactual with respect to either Bob or Charlie. We prove its security against a general incoherent attack, where Eve attacks single particles.Comment: 6 pages, 2 figures, close to the published versio

Blockchain for Healthcare: Securing Patient Data and Enabling Trusted Artificial Intelligence

Advances in information technology are digitizing the healthcare domain with the aim of improved medical services, diagnostics, continuous monitoring using wearables, etc., at reduced costs. This digitization improves the ease of computation, storage and access of medical records which enables better treatment experiences for patients. However, it comes with a risk of cyber attacks and security and privacy concerns on this digital data. In this work, we propose a Blockchain based solution for healthcare records to address the security and privacy concerns which are currently not present in existing e-Health systems. This work also explores the potential of building trusted Artificial Intelligence models over Blockchain in e-Health, where a transparent platform for consent-based data sharing is designed. Provenance of the consent of individuals and traceability of data sources used for building and training the AI model is captured in an immutable distributed data store. The audit trail of the data access captured using Blockchain provides the data owner to understand the exposure of the data. It also helps the user to understand the revenue models that could be built on top of this framework for commercial data sharing to build trusted AI models

Privacy-preserving data analytics in cloud computing

The evolution of digital content and rapid expansion of data sources has raised the need for streamlined monitoring, collection, storage and analysis of massive, heterogeneous data to extract useful knowledge and support decision-making mechanisms. In this context, cloud computing o↵ers extensive, cost-e↵ective and on demand computing resources that improve the quality of services for users and also help service providers (enterprises, governments and individuals). Service providers can avoid the expense of acquiring and maintaining IT resources while migrating data and remotely managing processes including aggregation, monitoring and analysis in cloud servers. However, privacy and security concerns of cloud computing services, especially in storing sensitive data (e.g. personal, healthcare and financial) are major challenges to the adoption of these services. To overcome such barriers, several privacy-preserving techniques have been developed to protect outsourced data in the cloud. Cryptography is a well-known mechanism that can ensure data confidentiality in the cloud. Traditional cryptography techniques have the ability to protect the data through encryption in cloud servers and data owners can retrieve and decrypt data for their processing purposes. However, in this case, cloud users can use the cloud resources for data storage but they cannot take full advantage of cloud-based processing services. This raises the need to develop advanced cryptosystems that can protect data privacy, both while in storage and in processing in the cloud. Homomorphic Encryption (HE) has gained attention recently because it can preserve the privacy of data while it is stored and processed in the cloud servers and data owners can retrieve and decrypt their processed data to their own secure side. Therefore, HE o↵ers an end-to-end security mechanism that is a preferable feature in cloud-based applications. In this thesis, we developed innovative privacy-preserving cloud-based models based on HE cryptosystems. This allowed us to build secure and advanced analytic models in various fields. We began by designing and implementing a secure analytic cloud-based model based on a lightweight HE cryptosystem. We used a private resident cloud entity, called ”privacy manager”, as an intermediate communication server between data owners and public cloud servers. The privacy manager handles analytical tasks that cannot be accomplished by the lightweight HE cryptosystem. This model is convenient for several application domains that require real-time responses. Data owners delegate their processing tasks to the privacy manager, which then helps to automate analysis tasks without the need to interact with data owners. We then developed a comprehensive, secure analytical model based on a Fully Homomorphic Encryption (FHE), that has more computational capability than the lightweight HE. Although FHE can automate analysis tasks and avoid the use of the privacy manager entity, it also leads to massive computational overhead. To overcome this issue, we took the advantage of the massive cloud resources by designing a MapReduce model that massively parallelises HE analytical tasks. Our parallelisation approach significantly speeds up the performance of analysis computations based on FHE. We then considered distributed analytic models where the data is generated from distributed heterogeneous sources such as healthcare and industrial sensors that are attached to people or installed in a distributed-based manner. We developed a secure distributed analytic model by re-designing several analytic algorithms (centroid-based and distribution-based clustering) to adapt them into a secure distributed-based models based on FHE. Our distributed analytic model was developed not only for distributed-based applications, but also it eliminates FHE overhead obstacle by achieving high efficiency in FHE computations. Furthermore, the distributed approach is scalable across three factors: analysis accuracy, execution time and the amount of resources used. This scalability feature enables users to consider the requirements of their analysis tasks based on these factors (e.g. users may have limited resources or time constrains to accomplish their analysis tasks). Finally, we designed and implemented two privacy-preserving real-time cloud-based applications to demonstrate the capabilities of HE cryptosystems, in terms of both efficiency and computational capabilities for applications that require timely and reliable delivery of services. First, we developed a secure cloud-based billing model for a sensor-enabled smart grid infrastructure by using lightweight HE. This model handled billing analysis tasks for individual users in a secure manner without the need to interact with any trusted parties. Second, we built a real-time secure health surveillance model for smarter health communities in the cloud. We developed a secure change detection model based on an exponential smoothing technique to predict future changes in health vital signs based on FHE. Moreover, we built an innovative technique to parallelise FHE computations which significantly reduces computational overhead

The Communication Complexity of Threshold Private Set Intersection

Threshold private set intersection enables Alice and Bob who hold sets $A$ and $B$ of size $n$ to compute the intersection $A \cap B$ if the sets do not differ by more than some threshold parameter $t$. In this work, we investigate the communication complexity of this problem and we establish the first upper and lower bounds. We show that any protocol has to have a communication complexity of $\Omega(t)$. We show that an almost matching upper bound of $\tilde{\mathcal{O}}(t)$ can be obtained via fully homomorphic encryption. We present a computationally more efficient protocol based on weaker assumptions, namely additively homomorphic encryption, with a communication complexity of $\tilde{\mathcal{O}}(t^2)$. We show how our protocols can be extended to the multiparty setting. For applications like biometric authentication, where a given fingerprint has to have a large intersection with a fingerprint from a database, our protocols may result in significant communication savings. We, furthermore, show how to extend all of our protocols to the multiparty setting. Prior to this work, all previous protocols had a communication complexity of $\Omega(n)$. Our protocols are the first ones with communication complexities that mainly depend on the threshold parameter $t$ and only logarithmically on the set size $n$