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Register a new userPerforming private database queries in a real-world environment using a quantum protocol
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In the well-studied cryptographic primitive 1-out-of-N oblivious transfer, a user retrieves a single element from a database of size N without the database learning which element was retrieved. While it has previously been shown that a secure implementation of 1-out-of-N oblivious transfer is impossible against arbitrarily powerful adversaries, recent research has revealed an interesting class of private query protocols based on quantum mechanics in a cheat sensitive model. Specifically, a practical protocol does not need to guarantee that database cannot learn what element was retrieved if doing so carries the risk of detection. The latter is sufficient motivation to keep a database provider honest. However, none of the previously proposed protocols could cope with noisy channels. Here we present a fault-tolerant private query protocol, in which the novel error correction procedure is integral to the security of the protocol. Furthermore, we present a proof-of-concept demonstration of the protocol over a deployed fibre.
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Private queries allow a user Alice to learn an element of a database held by a provider Bob without revealing which element she was interested in, while limiting her information about the other elements. We propose to implement private queries based on a quantum key distribution protocol, with changes only in the classical post-processing of the key. This approach makes our scheme both easy to implement and loss-tolerant. While unconditionally secure private queries are known to be impossible, we argue that an interesting degree of security can be achieved, relying on fundamental physical principles instead of unverifiable security assumptions in order to protect both user and database. We think that there is scope for such practical private queries to become another remarkable application of quantum information in the footsteps of quantum key distribution.
To evade the well-known impossibility of unconditionally secure quantum two-party computations, previous quantum private comparison protocols have to adopt a third party. Here we study how far we can go with two parties only. We propose a very feasible and efficient protocol. Intriguingly, although the average amount of information leaked cannot be made arbitrarily small, we find that it never exceeds 14 bits for any length of the bit-string being compared.
Mobile apps and location-based services generate large amounts of location data that can benefit research on traffic optimization, context-aware notifications and public health (e.g., spread of contagious diseases). To preserve individual privacy, one must first sanitize location data, which is commonly done using the powerful differential privacy (DP) concept. However, existing solutions fall short of properly capturing density patterns and correlations that are intrinsic to spatial data, and as a result yield poor accuracy. We propose a machine-learning based approach for answering statistical queries on location data with DP guarantees. We focus on countering the main source of error that plagues existing approaches (namely, uniformity error), and we design a neural database system that models spatial datasets such that important density and correlation features present in the data are preserved, even when DP-compliant noise is added. We employ a set of neural networks that learn from diverse regions of the dataset and at varying granularities, leading to superior accuracy. We also devise a framework for effective system parameter tuning on top of public data, which helps practitioners set important system parameters without having to expend scarce privacy budget. Extensive experimental results on real datasets with heterogeneous characteristics show that our proposed approach significantly outperforms the state of the art.
Since unconditionally secure quantum two-party computations are known to be impossible, most existing quantum private comparison (QPC) protocols adopted a third party. Recently, we proposed a QPC protocol which involves two parties only, and showed that although it is not unconditionally secure, it only leaks an extremely small amount of information to the other party. Here we further propose the device-independent version of the protocol, so that it can be more convenient and dependable in practical applications.
Quantum private comparison (QPC) aims to solve Tierce problem based on the laws of quantum mechanics, where the Tierce problem is to determine whether the secret data of two participants are equal without disclosing the data values. In this paper, we study for the fist time the utility of eight-qubit entangled states for QPC by proposing a new protocol. The proposed protocol only adopts necessary quantum technologies such as preparing quantum states and quantum measurements without using any other quantum technologies (e.g., entanglement swapping and unitary operations), which makes the protocol have advantages in quantum device consumption. The measurements adopted only include single-particle measurements, which is easier to implement than entangled-state measurements under the existing technical conditions. The proposed protocol takes advantage of the entanglement characteristics of the eight-qubit entangled state, and uses joint computation, decoy photon technology, the keys generated by quantum key distribution to ensure data privacy.
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