Ciphertext-Policy Attribute-Based Encrypted Data Equality Test and Classification

Thanks to the ease of access and low expenses, it is now popular for people to store data in cloud servers. To protect sensitive data from being leaked to the outside, people usually encrypt the data in the cloud. However, management of these encrypted data becomes a challenging problem, e.g. data classification. Besides, how to selectively share data with other users is also an important and interesting problem in cloud storage. In this paper, we focus on ciphertext-policy attribute based encryption with equality test (CP-ABEET). People can use CP-ABEET to implement not only flexible authorization for the access to encrypted data, but also efficient data label classification, i.e. test of whether two encrypted data contain the same message. We construct an efficient CP-ABEET scheme, and prove its security based on a reasonable number-theoretic assumption. Compared with the only existing CP-ABEET scheme, our construction is more efficient in key generation, and has shorter attribute-related secret keys and better security

Norwegian School of Economics and Business Administration, Shifting Capital Markets and Performance conference at Yale University, Texas Finance Festival

Abstract Since World War II, direct stock ownership by households has largely been replaced by indirect stock ownership by financial institutions which manage pensions. We argue that tax policy is the driving force. Using long time-series from eight countries, we show that the fraction of household ownership decreases with measures of the tax benefits of holding stocks inside a pension plan. This finding is important for policy considerations on effective taxation and for financial economics research on the long-term effects of taxation on corporate finance and asset prices

Attribute-Based Equality Test over Encrypted Data without Random Oracles

© 2013 IEEE. Sensitive data would be encrypted before uploading to the cloud due to the privacy issue. However, how to compare the encrypted data efficiently becomes a problem. Public Key Encryption with Equality Test (PKEET) provides an efficient way to check whether two ciphertexts (of possibly different users) contain the same message without decryption. As an enhanced variant, Attribute-based Encryption with Equality Test (ABEET) provides a flexible mechanism of authorization on the equality test. Most of the existing ABEET schemes are only proved to be secure in the random oracle model. Their security, however, would not be guaranteed if random oracles are replaced with real-life hash functions. In this work, we propose a construction of CP-ABEET scheme and prove its security based on some reasonable assumptions in the standard model. We then show how to modify the scheme to outsource complex computations in decryption and equality test to a third-party server in order to support thin clients

Outsourced ciphertext-policy attribute-based encryption with equality test

In the cloud era people get used to store their data to the cloud server, and would use encryption technique to protect their sensitive data from leakage. However, encrypted data management is a challenging problem, for example, encrypted data classification. Besides, how to effectively control the access to the encrypted data is also an important problem. Ciphertext-policy attribute-based encryption with equality test (CP-ABEET) is an efficient solution to the aforementioned problems, which enjoys the advantage of attribute-based encryption, and in the meanwhile supports the test of whether two different ciphertexts contain the same message without the need of decryption. However, the existing CP-ABEET schemes suffer from high computation costs. In this paper, we study how to outsource the heavy computation in CP-ABEET scheme to a third-party server. We introduce the notion of CP-ABEET supporting outsourced decryption (OCP-ABEET), which saves a lot of local computation loads of CP-ABEET. We propose a concrete construction of OCP-ABEET, and prove its security based on a reasonable number-theoretic assumption in the random oracle model. Compared with the existing CP-ABEET schemes, our scheme is more computationally efficient

Generic Construction of Privacy-Preserving Optimistic Fair Exchange Protocols

Privacy-preserving optimistic fair exchange (P2OFE) is a kind of protocols which aim to solve the fairness problem in the exchange of digital signatures between two parties and in the meanwhile protect their privacy. In P2OFE, no one else including the semi-trusted third party in charge of arbitration can confirm an exchange even after resolving a dispute. In this paper we present a black-box construction of P2OFE based on a tag-based public key encryption scheme and a standard digital signature scheme. Our construction follows the ‘sign-then-encrypt’ paradigm, and is secure in the standard model. Our construction is generic and admits more instantiations of P2OFE

Ciphertext-Policy Attribute-Based Encrypted Data Equality Test and Classification

© 2019 The British Computer Society 2019. All rights reserved. For permissions, please e-mail: [email protected]. Thanks to the ease of access and low expenses, it is now popular for people to store data in cloud servers. To protect sensitive data from being leaked to the outside, people usually encrypt the data in the cloud. However, management of these encrypted data becomes a challenging problem, e.g. data classification. Besides, how to selectively share data with other users is also an important and interesting problem in cloud storage. In this paper, we focus on ciphertext-policy attribute based encryption with equality test (CP-ABEET). People can use CP-ABEET to implement not only flexible authorization for the access to encrypted data, but also efficient data label classification, i.e. test of whether two encrypted data contain the same message. We construct an efficient CP-ABEET scheme, and prove its security based on a reasonable number-theoretic assumption. Compared with the only existing CP-ABEET scheme, our construction is more efficient in key generation, and has shorter attribute-related secret keys and better security

An Energy-Efficient Timetable Optimization Approach in a Bi-DirectionUrban Rail Transit Line: A Mixed-Integer Linear Programming Model

The quick growth of energy consumption in urban rail transit has drawn much attention due to the pressure of both operational cost and environmental responsibilities. In this paper, the timetable is optimized with respect to the system cost of urban rail transit, which pays more attention to energy consumption. Firstly, we propose a Mixed-Integer Non-Linear Programming (MINLP) model including the non-linear objective and constraints. The objective and constraints are linearized for an easier process of solution. Then, a Mixed-Integer Linear Programming (MILP) model is employed, which is solved using the commercial solver Gurobi. Furthermore, from the viewpoint of system cost, we present an alternative objective to optimize the total operational cost. Real Automatic Fare Collection (AFC) data from the Changping Line of Beijing urban rail transit is applied to validate the model in the case study. The results show that the designed timetable could achieve about a 35% energy reduction compared with the maximum energy consumption and a 6.6% cost saving compared with the maximum system cost

Anatomical Landmark Detection Using a Feature-Sharing Knowledge Distillation-Based Neural Network

Existing anatomical landmark detection methods consider the performance gains under heavyweight network architectures, which lead to models tending to have poor scalability and cost-effectiveness. To solve this problem, state-of-the-art knowledge distillation (KD) methods are proposed. However, they only require the teacher model to guide the output of the final layer of the student model. In this way, the semantic information learned by the student model is very limited. Different from previous works, we propose a novel KD-based model-training strategy, named feature-sharing fast landmark detection (FSF-LD), which focuses on intermediate features and effectively transfers richer spatial information from the teacher model to the student model. Moreover, to generate richer and more reliable knowledge, we propose a multi-task learning structure to pretrain the teacher model before FSF-LD. Finally, a tiny and effective anatomical landmark detection model is obtained. We evaluate our proposed FSF-LD on a public 2D hand radiograph dataset, a public 2D cephalometric radiograph dataset and a private 2D hip radiograph dataset. On the 2D hand dataset, our FSF-LD has 11.7%, 12.1%, 12.0,% and 11.4% improvement on SDR (r = 2 mm, r = 2.5 mm, r = 3 mm, r = 4 mm) compared with other KD methods. The results suggest the superiority of FSF-LD in terms of model performance and cost-effectiveness. However, it is a challenge to further improve the detection accuracy of anatomical landmarks and realize the clinical application of the research results, which is also our next plan