In the rapidly growing digital economy, protecting intellectual property (IP) associated with digital products has become increasingly important. Within this context, machine learning (ML) models, being highly valuable digital assets, have gained significant attention for IP protection. This paper introduces a practical encryption-based framework called EncryIP, which seamlessly integrates a public-key encryption scheme into the model learning process. This approach enables the protected model to generate randomized and confused labels, ensuring that only individuals with accurate secret keys, signifying authorized users, can decrypt and reveal authentic labels. Importantly, the proposed framework not only facilitates the protected model to multiple authorized users without requiring repetitive training of the original ML model with IP protection methods but also maintains the model's performance without compromising its accuracy. Compared to existing methods like watermark-based, trigger-based, and passport-based approaches, EncryIP demonstrates superior effectiveness in both training protected models and efficiently detecting the unauthorized spread of ML models.

In the originally published version of this manuscript, there was an error in the affiliations. Affiliation 2 and 3 were inadvertently erroneously reversed. This has now been corrected.

Abstract Electronic voting (e-voting) has been studied for many years. Recently, researchers find that blockchain can provide an alternative secure platform for e-voting systems, because of its properties of tamper resistance and transparency. However, existing blockchain-based schemes either require central authorities to tally ballots or can only handle a limited number of voters. This paper tries to propose a self-tallying e-voting system, i.e. the public can verify the validity of all ballots and tally the ballots without a centralized authority. To achieve this goal, we solve two challenges, namely how to cancel out all random numbers used for ballots and to prove the validity of ballots using a non-interactive zero knowledge proof. Our scheme is proved to be secure and shown to be practical by experiments.

Summary Based on cloud servers' powerful storage and computing resources, users can store mass encrypted data in the cloud and outsource complex encryption computations to the cloud servers. Since cloud servers cannot be completely trusted, then data privacy and integrity are concerned about hot issues. We focus on the following problems in outsourced encryptions: how to protect the data privacy and how to check the integrity of data and the correctness of cloud server's outsourcing computations. In this paper, we at first propose a verifiable chaotic encryption based on Chebyshev polynomials. The scheme supports verifiable function for data integrity. To further improve the efficiency of the scheme, a corresponding outsourced encryption scheme is constructed, where the heavy overhead evaluations of Chebyshev polynomials are transferred from the user side to the cloud server. The outsourced encryption also provides the checkability for data integrity and correctness of cloud computations. The scheme is suitable for mobile users with limited computing resources. Moreover, the newly proposed scheme no longer depends upon the simple heuristic analysis. It achieves the indistinguishability under chosen‐ciphertext attacks (IND‐CCA) in the standard model based on the Chebyshev‐based Decisional Diffie‐Hellman (CDDH) assumption. Thus, we answer a long‐term open problem for building a chaotic encryption scheme with provable security in the sense of the IND‐CCA.

With the purpose of taking physical attacks into account in security proofs, leakage-resilient cryptography has been initiated. Recently, many leakage-resilient cryptographic primitives have been proposed. In this paper, we put forward the first leakage-resilient wicked identity-based encryption (wicked IBE) scheme. To achieve this goal, we first present a new wicked IBE scheme in the composite order groups. The security proof of this scheme is achieved via the dual system encryption technique. In contrast with existing wicked IBE schemes, the new proposal can be proved fully secure in the standard model, even when the maximum hierarchy depth is a polynomial in the security parameter. Moreover, its security is based on some standard assumptions in the composite groups, which are independent of the hierarchy depth of the scheme. Based on this newly proposed scheme, we then put forward a fully secure leakage-resilient wicked IBE scheme in the bounded memory-leakage model. The leakage here is not only allowed on the user's secret key, but also on the master secret key. Its security is proved in the standard model by a hybrid argument in a sequence of computationally indistinguishable games. To the best of our knowledge, this is the first wicked IBE scheme in the context of leakage resilience.

In this paper, we propose the notion of n-evasive all-but-many lossy trapdoor functions ABM-LTFs, which is an extended abstraction of all-but-n lossy trapdoor functions ABN-LTFs proposed by Hemenway et al. in Asiacrypt 2011 and, at the same time, is a special case of ABM-LTFs proposed by Hofheinz in Eurocrypt 2012. We show two constructions of n-evasive ABM-LTFs. The first one is based on the decisional composite residuosity DCR assumption, and the second one is from chameleon hash functions and ABN-LTFs. Both of the constructions are based on reasonable assumptions with tight security reductions. Similar to ABN-LTFs and ABM-LTFs, n-evasive ABM-LTFs can be employed to construct indistinguishability-based selective opening chosen-ciphertext secure public-key encryption PKE schemes and may have other applications in cryptography. The instantiation of n-evasive ABM-LTFs can be based on the well-known assumption, for example, DCR, and the security reduction is much tighter than that of ABM-LTFs. On the other hand, the black-box PKE construction from n-evasive ABM-LTFs is more general than that from ABN-LTFs. Copyright © 2014 John Wiley & Sons, Ltd.