With the advent of cloud computing, traditional machine learning (ML) are migrating into cloud-based ML day by day following the concept of machine learning as a cloud service, which enables multiple entities to contribute to and benefit from shared datasets and models. As well as training the linear classification model, training the nonlinear classification model is also an essential task in cloud-based ML. However, this task commonly involves learning knowledge from different datasets provided by various entities, which often contain sensitive information like patients' physiological indices. Therefore, it gives rise a natural question how to allow multiple users collaboratively participating in a nonlinear classification task while preserving these datas' privacy. As a promising cryptographic tool, the concept of unbounded multiinput functional encryption can be developed to answer such a question, such as google search engines are running over this concept-based ML approaches. However, most of existing approaches are derived from this concept with inner product functionality, specifying for a linear classification model and thus fails to cope with a non-linear classification one. In this paper, we introduce an advanced cryptographic concept called unbounded multi-input quadratic functional encryption, and give a concrete construction which allows arbitrary number of users participating in the classifying tasks with a nonlinear classification model but without divulging their private data. Moreover, we provide a strict mathematical security proof under a well-defined security model as well as some security attacks are analyzed, followed by an experimental analysis and comparison on a real dateset as well as a practical use case to demonstrate our scheme's performance.

Feature unlearning, forgetting sensitive features while maintaining the accuracy of models, is a pressing issue against Feature Inference Attacks (FIA) in Vertical Federated Learning (VFL). This issue is addressed by retraining the model from scratch on a dataset without the sensitive features from scratch or Federated Unlearning (FU) for all samples. However, they either introduce high overheads due to retraining or reduce the accuracy of the unlearned model. In this paper, we proposedCer-FeaUn, a certified feature unlearning, trading off between the overheads and accuracy. Specifically, a novelfeature perturbation strategyis first proposed to construct a perturbed dataset, where the sensitive features are perturbed with noises. Then, the effect is defined as the parameters difference between models trained with the original and perturbed dataset. Finally, an unlearned model is trained in first-order, where the effect is removed from the original model in one epoch. Furthermore, Cer-FeaUn performscertified removalfor server-side models with strongly convex loss functions. That is, the distribution of the unlearned model is statistically indistinguishable from that of the retrained model. For the scenario with a few sensitive features, simulation results show that the accuracy of the unlearned model is up to 84.79%, and the runtime of Cer-FeaUn is 15 times faster than that of the retrained model.

Location sharing, as an essential component of mobile applications, helps mobile users share location information and enhance their community connections. However, users may be reluctant to share their locations with personal privacy concerns, as anyone including location server who knows these locations can infer much sensitive information about users through analyzing these locations’ information plus their background knowledge. Therefore, it poses a natural question for mobile location-sharing applications how to share users’ locations without any breach of their privacy. To answer this question, in this article, we describe two practical attribute-based encryption schemes served privacy-preserving mobile location-sharing applications. Our proposals are quite suitable for such a mobile application—location sharing with common interests since in our designs users’ interests are also taken into consideration as well as location information. In particular, our two schemes have a higher performance in the sense that in our first construction both ciphertexts and private keys are of constant size simultaneously, and our second construction is an extension of the first that provides a tradeoff between ciphertext size and public-key size. Therefore, the two schemes we designed in this work are quite practical in mobile applications which are often equipped with limited transmission or storage resources. Finally, we offer a formal security proof under a well-defined security model, followed by an experimental evaluation and a theoretical performance comparison.

As a common metric, maximum similarity between two objects is widely employed by web platforms to provide matching services. However, the calculation of maximum similarity involves numerous sensitive or confidential users’ data, and the web platform server is often not trusted who might peep these data out of curiosity, or even worse sell them to unauthorized entities to make profits. Therefore, many research lines on functional encryption have been suggested and studied on how to calculate the maximum similarity while ensure the privacy of users’ data. Unfortunately, all of them will divulge some intermediate results to the web platform server when processing this issue. In this paper we present a new functional encryption scheme supporting privacy-preserving maximum similarity, which enables the web service platforms to figure out the maximum similarity without learning anything else about their data. Moreover, we provide a formal analysis to prove the security of the proposed scheme, followed by some experimental evaluations and comprehensive comparisons with the related works. It shows that, our scheme is the first functional encryption realization on maximum similarity without divulging the intermediate result and meanwhile achieve a higher security-function privacy, as well as a traditional data privacy.

Privacy-preserving data evaluation is one of the prominent research topics in the Big Data era. In many data evaluation applications that involve sensitive information, such as the medical records of patients in a medical system, protecting data privacy during the data evaluation process has become an essential requirement. Aiming at solving this problem, numerous fuzzy encryption systems for different similarity metrics have been proposed in literature. Unfortunately, the existing fuzzy encryption systems either fail to achieve attribute-hiding or achieve it, but are impractical. In this article, we propose a new fuzzy encryption scheme for privacy-preserving data evaluation based on overlap distance, which can work in an integer domain while achieving attribute-hiding. In particular, we develop a novel approach to enable an accurate overlap distance to be fast calculated. This technique makes the number of pairing operations during decryption stage negative correlation with the size of the threshold, which is pretty practical for some applications especially with a large threshold. Additionally, we provide a formal security analysis of the proposed scheme, followed by a comprehensive experimental. Also we show that our scheme can be well applied to some scenarios, such as fuzzy keyword searchable encryption and attribute-hiding closest substring encryption.

Abstract As a prominent cryptographic tool, geometric searchable encryption (GSE) can be applied in many scenarios, such as location-based services (LBS), social networks and vehicle networks. Unfortunately, most of existing searchable encryption schemes supporting the functionality of geometric range searches suffer from false positives, which will lead people to make a wrong decision and further raise some serious consequences such as financial loss. In addition, some of them are designed under a symmetric system, which is not enough flexible deployed in LBS since in a symmetric system only a private key holder creates ciphertext, whereas in a public-key system anyone who holds a public key can produce ciphertext. In this paper, we intend to design a novel GSE scheme without any false positive under a public-key system supporting arbitrary geometric area searches, which is able to guarantee an accurate query result. Toward this goal, we develop a novel technique in handling the relation between a point and any convex polygon in combination with an inner product encryption, which is able to support arbitrary convex polygon range searches without any false positive. A comprehensive experiment demonstrates that, compared with the known schemes, our scheme possesses a 100% accuracy as well as an acceptable efficiency in the sense that it can guarantee that all files retrieved by users are exactly matched ones. Finally, we provide two practical examples of our GSE scheme: privacy-preserving friend-nearby notification with a common point of interest and privacy-preserving parking monitor and guiding system.

Location data play an important role in location-based services (LBS) since they can help a service provider analyze users’ daily activities and further derive users’ behavioral patterns. However, in the meanwhile the use of the location data in LBS can also enable the service provider to track users’ journeys which will reveal their personal information, such as house address and places of interest they visited, all of which users are usually unwilling to disclose. Therefore, security and privacy incidents can and do occur often. In this article, aiming at how to hide location privacy and meanwhile provide an accurate search for LBS, we design a new geometric searchable encryption scheme under a public-key system (a.k.a. asymmetric system) for different types of geometric range queries, which enables the service provider to respond to the range queries from users accurately without learning the information about users’ location. Towards this goal, we first exploit a novel encoding, and then develop some sophisticated transformations in combination with a special pairing function, which eventually converts different types of geometric range queries in LBS into different inner product problems. Finally, we present our construction by virtue of the techniques above with the inner product encryption technique. In addition, a comprehensive experimental analysis running on a specific application of privacy-preserving LBS and comparison demonstrate, our proposal is able to guarantee a higher accuracy for users’ range queries without any false positive, and meanwhile express richer queries. Furthermore, we show another application of our proposal to privacy-preserving remote medical diagnosis at the end of this article.

Summary For fine‐grained data access control in cloud computing, for the first time, we introduce a new concept called restricted proxy re‐encryption with keyword search, which combines the function of proxy re‐encryption with keyword search and that of threshold cryptosystem. To demonstrate this concept, we present the formal syntax for restricted proxy re‐encryption with keyword search, the security model, and a concrete construction. In our scheme, we take advantage of the techniques of threshold cryptosystem to restrict the capacity of the proxy cloud server, and in the meantime, we let the proxy cloud server can only re‐encrypt the data containing a specified keyword, which matches the trapdoor from delegatee to provide an accurate access control for users. While in this process, the proxy cloud server learns nothing about the contents of data and keyword. Our scheme is proved to be semantically secure under the modified bilinear Diffie–Hellman assumption and the q‐decisional bilinear Diffie–Hellman inversion assumption in the random oracle model. Finally, we apply the techniques in our scheme to some practical problems. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract In this paper, a distributed secret share update scheme with public verifiability for ad hoc network is proposed, in which the system secret key is collaboratively generated by k nodes or more, instead of by a centralized key generation center. To prevent a passive adversary from collecting other nodes' shares to compromise the system key over a long period, each node can periodically refresh its share without changing the system key. At the same time, to resist an active adversary to forge partial share and even to solve the accusation problem, any one can publicly verify the correctness of partial shares submitted by other nodes in the share update phase. To achieve our goals, we explore the technique of verifiable encryption with additive homomorphism and that of threshold cryptography. The analysis shows that the proposed scheme is more secure and efficient than the previous schemes for ad hoc networks. Copyright © 2014 John Wiley & Sons, Ltd.

Abstract There are many researches on the polynomial‐based verifiable (k,n) multi‐secret sharing scheme (VMSSS), but none of them focuses on the Chinese remainder theorem (CRT)‐based VMSSS so far. For the first time, we provide a cheater identifiable multi‐secret sharing scheme based on CRT as an alternative method for VMSSS, which is unconditionally secure when the number of cheaters t≤(k − 1)/3. We adopt an encoding method, which makes multiple secrets to be transferred as a single one. In addition, we utilize a single keyed message authenticated code (MAC) to detect and identify cheaters in the reconstruction phase. Then, combine these two methods with a CRT‐based Asmuth‐Bloom's SSS to achieve our design goals. In our scheme, all participants share a single key of MAC rather than each participant possesses an independent key to check the validity of shares, and the size of share is independent in any of n,k, and t. Analyses show that our scheme is more efficient and secure than existing ones. Finally, as an example of the practical impact of our work, we present how our techniques can be applied to secure sum computation. Copyright © 2015 John Wiley & Sons, Ltd.

ABSTRACT Broadcast encryption is an effective way to broadcast a message securely such that more than one privileged receiver can decrypt it. The well‐known constructions of identity‐based broadcast encryption only support bounded broadcast users that had to deploy the maximum user number in advance. This is somewhat inefficient and impractical if the broadcast user number is predetermined. In this paper, we propose an adaptively secure identity‐based broadcast encryption in the standard model that supports arbitrary number of users in broadcast set, which eliminates the size of public parameters with a constant number of group elements and obtain short ciphertexts, secret keys, and public parameters. We use the techniques of semi‐functional ciphertexts and semi‐functional keys in orthogonal subgroups to implement the boundless broadcast set and adaptive security by means of dual‐system encryption mechanism in a composite‐order group, and we prove the scheme to be fully secure without the random oracles in the static assumptions. The proposed scheme captures the properties of confidentiality, adaptive security, constant key, and short ciphertext. We also evaluate the computational costs and communication overheads and give the deployment in secure set‐top box broadcast systems. Copyright © 2012 John Wiley & Sons, Ltd.