Constructing Certificateless Encryption with Keyword Search against Outside and Inside Keyword Guessing Attacks

Searchable public key encryption is a useful cryptographic paradigm that enables an untrustworthy server to retrieve the encrypted data without revealing the contents of the data. It offers a promising solution to encrypted data retrieval in cryptographic cloud storage. Certificateless public key cryptography (CLPKC) is a novel cryptographic primitive that has many merits. It overcomes the key escrow problem in identity-based cryptography (IBC) and the cumbersome certificate problem in conventional public key cryptography (PKC). Motivated by the appealing features of CLPKC, several certificateless encryption with keyword search (CLEKS) schemes have been presented in the literature. But, our cryptanalysis demonstrates that the previously proposed CLEKS frameworks suffer from the security vulnerability caused by the keyword guessing attack. To remedy the security weakness in the previous frameworks and provide resistance against both inside and outside keyword guessing attacks, we propose a new CLEKS framework. Under the new framework, we design a concrete CLEKS scheme and formally prove its security in the random oracle model. Compared with previous two CLEKS schemes, the proposed scheme has better overall performance while offering stronger security guarantee as it withstands the existing known types of keyword guessing attacks.

On k-Error Linear Complexity of Some Explicit Nonlinear Pseudorandom Sequences

Combining with the research on the linear complexity of explicit nonlinear generators of pseudorandom sequences, we study the stability on linear complexity of two classes of explicit inversive generators and two classes of explicit nonlinear generators. We present some lower bounds in theory on the k-error linear complexity of these explicit generatol＇s, which further improve the cryptographic properties of the corresponding number generators and provide very useful information when they are applied to cryptography.

An Efficient and Privacy-Preserving Data Aggregation Scheme Supporting Arbitrary Statistical Functions in IoT

The Internet of Things(IoT)has profoundly impacted our lives and has greatly revolutionized our lifestyle.The terminal devices in an IoT data aggregation application sense real-time data for the remote cloud server to achieve intelligent decisions.However,the high frequency of collecting user data will raise people concerns about personal privacy.In recent years,many privacy-preserving data aggregation schemes have been proposed.Unfortunately,most existing schemes cannot support either arbitrary aggregation functions,or dynamic user group management,or fault tolerance.In this paper,we propose an efficient and privacy-preserving data aggregation scheme.In the scheme,we design a lightweight encryption method to protect the user privacy by using a ring topology and a random location sequence.On this basis,the proposed scheme supports not only arbitrary aggregation functions,but also flexible dynamic user management.Furthermore,the scheme achieves faulttolerant capabilities by utilizing a future data buffering mechanism.Security analysis reveals that the scheme can achieve the desired security properties,and experimental evaluation results show the scheme's efficiency in terms of computational and communication overhead.

Trust Model Based on Structured Protection for High Level Security System

In order to ensure the security of information systems, it's essential to make sure that system behaviors are trusted. By analyzing threats that exist in executing procedures, a trust model based on structured protection is proposed. We consider that functional components, system actions and message flows between components are three key factors of information systems. Structured protection requirements on components, connections and action parameters are also provided. Four trusted properties of the model are deducted through formal analysis, and trusted system behavior is defined based on these properties. Furthermore, decision theorem of trusted system behavior is proved. The developed prototype system indicates the model is practical. It is a general theory model built on logic deduction and independent on specific environment and the behaviors of the system designed and implemented following the model are trusted.

Achieving dynamic privacy measurement and protection based on reinforcement learning for mobile edge crowdsensing of IoT

With the maturity and development of 5G field,Mobile Edge CrowdSensing(MECS),as an intelligent data collection paradigm,provides a broad prospect for various applications in IoT.However,sensing users as data uploaders lack a balance between data benefits and privacy threats,leading to conservative data uploads and low revenue or excessive uploads and privacy breaches.To solve this problem,a Dynamic Privacy Measurement and Protection(DPMP)framework is proposed based on differential privacy and reinforcement learning.Firstly,a DPM model is designed to quantify the amount of data privacy,and a calculation method for personalized privacy threshold of different users is also designed.Furthermore,a Dynamic Private sensing data Selection(DPS)algorithm is proposed to help sensing users maximize data benefits within their privacy thresholds.Finally,theoretical analysis and ample experiment results show that DPMP framework is effective and efficient to achieve a balance between data benefits and sensing user privacy protection,in particular,the proposed DPMP framework has 63%and 23%higher training efficiency and data benefits,respectively,compared to the Monte Carlo algorithm.

Construction of Odd-Variable Boolean Function with Maximum Algebraic Immunity Using Univariate Polynomial Representation

To protect against algebraic attacks, a high algebraic immunity is now an important criterion for Boolean functions used in stream ciphers. In this paper, a new method based on a univariate polynomial representation of Boolean functions is proposed. The proposed method is used to construct Boolean functions with an odd number of variables and with maximum algebraic immunity. We also discuss the nonlinearity of the constructed functions. Moreover, a lower bound is determined for the number of Boolean functions with maximum algebraic immunity.

Achieving reliable and anti-collusive outsourcing computation and verification based on blockchain in 5G-enabled IoT

Widespread applications of 5G technology have prompted the outsourcing of computation dominated by the Internet of Things(IoT)cloud to improve transmission efficiency,which has created a novel paradigm for improving the speed of common connected objects in IoT.However,although it makes it easier for ubiquitous resource-constrained equipment that outsources computing tasks to achieve high-speed transmission services,security concerns,such as a lack of reliability and collusion attacks,still exist in the outsourcing computation.In this paper,we propose a reliable,anti-collusion outsourcing computation and verification protocol,which uses distributed storage solutions in response to the issue of centralized storage,leverages homomorphic encryption to deal with outsourcing computation and ensures data privacy.Moreover,we embed outsourcing computation results and a novel polynomial factorization algorithm into the smart contract of Ethereum,which not only enables the verification of the outsourcing result without a trusted third party but also resists collusion attacks.The results of the theoretical analysis and experimental performance evaluation demonstrate that the proposed protocol is secure,reliable,and more effective compared with state-of-the-art approaches.

Modified constructions of binary sequences using multiplicative inverse

Two new families of finite binary sequences are constructed using multiplicative inverse. The sequences are shown to have strong pseudorandom properties by using some estimates of certain exponential sums over finite fields. The constructions can be implemented fast since multiplicative inverse over finite fields can be computed in polynomial time.

Achieve Personalized Anonymity Through Query Blocks Exchanging

In cyberspace security,the privacy in location-based services(LBSs) becomes more critical. In previous solutions,a trusted third party(TTP) was usually employed to provide disturbance or obfuscation,but it may become the single point of failure or service bottleneck. In order to cope with this drawback,we focus on another important class,establishing anonymous group through short-range communication to achieve k-anonymity with collaborative users. Along with the analysis of existing algorithms,we found users in the group must share the same maximum anonymity degree,and they could not ease the process of preservation in a lower one. To cope with this problem,we proposed a random-QBE algorithm to put up with personalized anonymity in user collaboration algorithms,and this algorithm could preserve both query privacy and location privacy. Then we studied the attacks from passive and active adversaries and used entropy to measure user's privacy level. Finally,experimental evaluations further verify its effectiveness and efficiency.

Linear complexity of quaternary sequences with odd period and low autocorrelation

Equivalence between two classes of quaternary sequences with odd period and best known autocorrelation are proved. A lower bound on the linear complexity of these sequences is presented. It is shown that the quaternary sequences have large linear complexity to resist Reeds and Sloane algorithm attack effectively.

Provably Secure Self-Certified Signature Schemes with Message Recovery

To solve the key escrow problem of the identity-based cryptosystem, Girault introduced the notion of a self-certified public key, which not only eliminates the need to authenticate a public key but also solves the key escrow problem. This paper proposes a Self-Certified Signature (SCS) scheme with message recovery and two variants without using bilinear pairings: one is the authenticated encryption scheme in which only the designated receiver can verify the signature, and the other is the authenticated encryption scheme with message linkage that deals with large messages. These three SCS schemes are provably secure in the random oracle model and are more efficient than previous schemes.

Forgeability of Wang-Zhu-Feng-Yau’s Attribute-Based Signature with Policy-and-Endorsement Mechanism

Recently, Wang et al. presented a new construction of attribute-based signature with policy-and-endorsement mechanism. The existential unforgeability of their scheme was claimed to be based on the strong Diffie-Hellman assumption in the random oracle model. Unfortunately, by carefully revisiting the design and security proof of Wang et alfs scheme, we show that their scheme cannot provide unforgeability, namely, a forger, whose attributes do not satisfy a given signing predicate, can also generate valid signatures. We also point out the flaws in Wang et al.＇s proof.

New classes of sequence families with low correlation by using multiplicative and additive characters

For an odd prime p, a new sequence family of period prom- 1, size （M-1）pmr is proposed using multi-plicative and additive characters. The upper bound for the maximum magnitude of nontrivial correlations of the sequence family is derived using well-known character sums. The upper bound is shown to be （r ＋ 1）√pm ＋ 3, which meets the Welch bound asymptotically.

Towards Public Integrity Audition for Cloud-IoT Data Based on Blockchain

With the rapidly developing of Internet of Things (IoT), the volume ofdata generated by IoT systems is increasing quickly. To release the pressure ofdata management and storage, more and more enterprises and individuals preferto integrate cloud service with IoT systems, in which the IoT data can be outsourced to cloud server. Since cloud service provider (CSP) is not fully trusted,a variety of methods have been proposed to deal with the problem of data integritychecking. In traditional data integrity audition schemes, the task of data auditing isusually performed by Third Party Auditor (TPA) which is assumed to be trustful.However, in real-life TPA is not trusted as people thought. Therefore, theseschemes suffer from the underlying problem of single-point failure. Moreover,most of the traditional schemes are designed by RSA or bilinear map techniqueswhich consume heavy computation and communication cost. To overcome theseshortcomings, we propose a novel data integrity checking scheme for cloud-IoTdata based on blockchain technique and homomorphic hash. In our scheme, thetags of all data blocks are computed by a homomorphic hash function and storedin blockchain. Moreover, each step within the process of data integrity checking issigned by the performer, and the signatures are stored in blockchain through smartcontracts. As a result, each behavior for data integrity checking in our scheme canbe traced and audited which improves the security of the scheme greatly. Furthermore, batch-audition for multiple data challenges is also supported in our scheme.We formalize the system model of our scheme and give the concrete construction.Detailed performance analyses demonstrate that our proposed scheme is efficientand practical without the trust-assumption of TPA.

Privacy-preserving edge-assisted image retrieval and classification in IoT

Internet of Things (IoT) has drawn much attention in recent years. However, the image data captured by IoT terminal devices are closely related to users, personal information, which are sensitive and should be protected. Though traditional privacy-preserving outsourced computing solutions such as homomorphic cryptographic primitives can support privacy-preserving computing, they consume a significant amount of computation and storage resources. Thus, it becomes a heavy burden on IoT terminal devices with limited resources. In order to reduce the resource consumption of terminal device, we propose an edge-assisted privacy-preserving outsourced computing framework for image processing, including image retrieval and classification. The edge nodes cooperate with the terminal device to protect data and support privacy-preserving computing on the semitrusted cloud server. Under this framework, edge-assisted privacy-preserving image retrieval and classification schemes are proposed in this paper. The security analysis and performance evaluation show that the proposed schemes greatly reduce the computational, communication and storage burden of IoT terminal device while ensuring image data security.

Diagnosability of the Incomplete Star Graphs

The growing size of the multiprocessor systems increases their vulnerability to component failures. It is crucial to local and to replace the fault processors to maintain system’s high reliability. The fault diagnosis is the process of identifying faulty processors in a system through testing. This paper establishes the diagnosabilities of the incomplete star graph Sn (n≥4) with missing links under the PMC model and its variant, the BGM model, and shows that the diagnosabilities of incomplete star graph Sn under these two diagnostic models can be determined by the minimum degree of its topology structure. This method can also be applied to the other existing multiprocessor systems.

Generalized H-codes and type Ⅱ codes over GF(4)

The typeⅡ codes have been studied widely in applications since their appearance. With analysis of the algebraic structure of finite field of order 4 （i.e., GF（4））, some necessary and sufficient conditions that a generalized H-code （i.e., GH-code） is a type Ⅱ code over GF（4） are given in this article, and an efficient and simple method to generate type Ⅱ codes from GH-codes over GF（4） is shown. The conclusions further extend the coding theory of type Ⅱ.

Constructions of vector output Boolean functions with high generalized nonlinearity

Carlet et al. recently introduced generalized nonlinearity to measure the ability to resist the improved correlation attack of a vector output Boolean function. This article presents a construction of vector output Boolean fimctions with high generalized nonlinearity using the e-biased sample space. The relation between the resilient order and generalized nonlinearity is also discussed.

Efficient Batch Verification of Online/Offline Short Signature for a Multi-Signer Setting

In this paper, we propose a method to construct an online/offiine batch verification signature scheme in a multi-signer setting. The length of the scheme is approximately 480 bits. Based on the Lysyanskaya, Rivest, Sahai and Wolf （LRSW） assumption, this scheme is proved secure in a random oracle model, and it requires only three pairing operations for verifying n signatures from a multi-signer setting.

Inner product encryption from ring learning with errors

The functional encryption scheme designed using the lattice can realize fine-grained encryption and it can resist quantum attacks.Unfortunately,the sizes of the keys and ciphertexts in cryptographic applications based on learning with errors are large,which makes the algorithm inefficient.Therefore,we construct a functional encryption for inner product predicates scheme by improving the learning with errors scheme of Agrawal et al.[Asiacrypt 2011],and its security relies on the difficulty assumption of ring learning with errors.Our construction can reduce the sizes of the keys and ciphertexts compared with the learning with errors scheme.