Differential Game Model of Resource Extraction with Continuous and Dynamic Updating

This paper is devoted to a new class of differential games with continuous and dynamic updating.The direct application of resource extraction in a case of dynamic and continuous updating is considered.It is proved that the optimal control(cooperative strategies)and feedback Nash equilibrium strategies uniformly converge to the corresponding strategies in the game model with continuous updating as the number of updating instants converges to infinity.Similar results are presented for an optimal trajectory(cooperative trajectory),equilibrium trajectory and corresponding payoffs.

Searchable Encryption Cloud Storage with Dynamic Data Update to Support Efficient Policy Hiding

Ciphertext policy attribute based encryption(CP-ABE)can provide high finegrained access control for cloud storage.However,it needs to solve problems such as property privacy protection,ciphertext search and data update in the application process.Therefore,based on CP-ABE scheme,this paper proposes a dynamically updatable searchable encryption cloud storage(DUSECS)scheme.Using the characteristics of homomorphic encryption,the encrypted data is compared to achieve efficient hiding policy.Meanwhile,adopting linked list structure,the DUSECS scheme realizes the dynamic data update and integrity detection,and the search encryption against keyword guessing attacks is achieved by combining homomorphic encryption with aggregation algorithm.The analysis of security and performance shows that the scheme is secure and efficient.

Secure Multi-keyword Search Supporting Dynamic Update and Ranked Retrieval

As cloud computing is becoming prevalent, data owners are motivated to delegate complex data managements to the commercial cloud for economic savings. Sensitive data is usually encrypted before being uploaded to the cloud, which unfortunately makes the frequently-used search function a challenging problem. In this paper, we present a new multi-keyword dynamic search scheme with result ranking to make search over encrypted data more secure and practical. In the scheme, we employ a powerful function-hiding inner product encryption to enhance the security by preventing the leakage of search pattern. For the concern of efficiency, we adopt a tree-based index structure to facilitate the searching process and updating operations. A comprehensive security analysis is provided and experiments over the real world data show that our scheme is efficient.

Mode of Operation for Modification, Insertion, and Deletion of Encrypted Data

Due to the development of 5G communication,many aspects of information technology(IT)services are changing.With the development of communication technologies such as 5G,it has become possible to provide IT services that were difficult to provide in the past.One of the services made possible through this change is cloud-based collaboration.In order to support secure collaboration over cloud,encryption technology to securely manage dynamic data is essential.However,since the existing encryption technology is not suitable for encryption of dynamic data,a new technology that can provide encryption for dynamic data is required for secure cloudbased collaboration.In this paper,we propose a new encryption technology to support secure collaboration for dynamic data in the cloud.Specifically,we propose an encryption operation mode which can support data updates such as modification,addition,and deletion of encrypted data in an encrypted state.To support the dynamic update of encrypted data,we invent a new mode of operation technique named linked-block cipher(LBC).Basic idea of our work is to use an updatable random value so-called link to link two encrypted blocks.Due to the use of updatable random link values,we can modify,insert,and delete an encrypted data without decrypt it.

ACG-TypeMethod for InverseQuadratic Eigenvalue Problems in Model Updating of Structural Dynamics

In this paper we first present a CG-type method for inverse eigenvalue problem of constructing real and symmetric matrices M,D and K for the quadratic pencil Q(λ)=λ^(2)M+λD+K,so that Q(λ)has a prescribed subset of eigenvalues and eigenvectors.This method can determine the solvability of the inverse eigenvalue problem automatically.We then consider the least squares model for updating a quadratic pencil Q(λ).More precisely,we update the model coefficient matrices M,C and K so that(i)the updated model reproduces the measured data,(ii)the symmetry of the original model is preserved,and(iii)the difference between the analytical triplet(M,D,K)and the updated triplet(M_(new),D_(new),K_(new))is minimized.In this paper a computationally efficient method is provided for such model updating and numerical examples are given to illustrate the effectiveness of the proposed method.

TOAST: Automated Testing of Object Transformers in Dynamic Software Updates

Dynamic software update(DSU)patches programs on the fly.It often involves the critical task of object transformation that converts live objects of the old-version program to their semantically consistent counterparts under the new-version program.This task is accomplished by invoking an object transformer on each stale object.However,a defective transformer failing to maintain consistency would cause errors or even crash the program.We propose TOAST(Test Object trAnSformaTion),an automated approach to detecting potential inconsistency caused by object transformers.TOAST first analyzes an update to identify multiple target methods and then adopts a fuzzer with specially designed inconsistency guidance to randomly generate object states to drive two versions of a target method.This creates two corresponding execution traces and a pair of old and new objects.TOAST finally performs object transformation to create a transformed object and detects inconsistency between it and the corresponding new object produced from scratch by the new program.Moreover,TOAST checks behavior inconsistency by comparing the return variables and exceptions of the two executions.Experimental evaluation on 130 updates with default transformers shows that TOAST is promising:it got 96.0%precision and 85.7%recall in state inconsistency detection,and 81.4%precision and 94.6%recall in behavior inconsistency detection.The inconsistency guidance improved the fuzzing efficiency by 14.1%for state inconsistency detection and 40.5%for behavior inconsistency detection.