scholarly journals A Secure and Efficient Multi Authority Encryption Scheme in Cross Domain Data Sharing

2020 ◽  
Vol 9 (4) ◽  
pp. 1289-1293
Keyword(s):  
Current System ◽  
Data Set ◽  
Customer Data ◽  
Fine Grained ◽  
Computational Overhead ◽  
Cross Domain ◽  
Attribute Based Encryption ◽  
Dynamic Modification ◽  
Structure Flexibility ◽  
Cryptographic Techniques

Attribute Based Encryption (ABE) in light of the fact that the rule locals to make sure about the patient data set aside on a semi-trusted. In ABE plot, each patient is regularly recognized by name which fuses the patient attributes. At the reason when Patient re-proper the delicate data for sharing on cloud system on cloud structures. Taking care of the patient records on suspicious limit makes secure transfer of data to be a test issue. To remain tricky customer data mystery against suspicious cloud structure. the current system when in doubt apply cryptographic techniques by revealing data unscrambling keys just to affirmed customer. the basic troubles for cryptographic technique fuse at a proportional time achieving structure flexibility and fine-grained data get the opportunity to manage, gainful key or customer the load up, data security, computational overhead then forward. To manage these issues, promptly applied and maintaining access approaches snared in to attributes and sanctioning the information owner to designate most count genuine assignments to customer disavowal to untrusted server without uncovering data substance to around then. We achieve this target by introducing multi authority characteristic based encryption. Our proposed plot in like manner has momentous features of customer get the chance to benefit characterization, dynamic modification of access game plans or archive properties and customer puzzle key duty, supports capable on-demand customer or trademark denial and break-glass access under emergency circumstances.

scholarly journals Multi-Level Attribute-Based Encryption Access Control Scheme for Big Data

2018 ◽  
Vol 173 ◽  
pp. 03047
Author(s):  
Zhao Li ◽  
Shuiyuan Huan
Keyword(s):  
Big Data ◽  
Access Control ◽  
Data Processing ◽  
Data Access ◽  
Big Data Processing ◽  
Fine Grained ◽  
Computational Overhead ◽  
Data Access Control ◽  
Attribute Based Encryption ◽  
Multi Level

There are many security threats such as data’s confidentiality and privacy protection in the new application scenario of big data processing, and for the problems such as coarse granularity and low sharing capability existing in the current research on big data access control, a new model to support fine-grained access control and flexible attribute change is proposed. Based on CP-ABE method, a multi-level attribute-based encryption scheme is designed to solve fine-grained access control problem. And to solve the problem of attribute revocation, the technique of re-encryption and version number tag is integrated into the scheme. The analysis shows that the proposed scheme can meet the security requirement of access control in big data processing environment, and has an advantage in computational overhead compared with the previous schemes.

Fine-Grained Access Control with Efficient Revocation in Cloud Storage

2014 ◽  
Vol 571-572 ◽  
pp. 79-89
Author(s):  
Ting Zhong ◽  
You Peng Sun ◽  
Qiao Liu
Keyword(s):  
Access Control ◽  
Cloud Storage ◽  
Storage System ◽  
Fine Grained ◽  
Computational Overhead ◽  
Attribute Based Encryption ◽  
Encrypt Data ◽  
User Revocation ◽  
Ciphertext Policy ◽  
Access Policies

In the cloud storage system, the server is no longer trusted, which is different from the traditional storage system. Therefore, it is necessary for data owners to encrypt data before outsourcing it for sharing. Simultaneously, the enforcement of access policies and support of policies updates becomes one of the most challenging issues. Ciphertext-policy attribute-based encryption (CP-ABE) is an appropriate solution to this issue. However, it comes with a new obstacle which is the attribute and user revocation. In this paper, we propose a fine-grained access control scheme with efficient revocation based on CP-ABE approach. In the proposed scheme, we not only realize an efficient and immediate revocation, but also eliminate some burden of computational overhead. The analysis results indicate that the proposed scheme is efficient and secure for access control in cloud storage systems.

scholarly journals Cross-Genre and Cross-Domain Detection of Semantic Uncertainty

2012 ◽  
Vol 38 (2) ◽  
pp. 335-367 ◽  
Author(s):  
György Szarvas ◽  
Veronika Vincze ◽  
Richárd Farkas ◽  
György Móra ◽  
Iryna Gurevych
Keyword(s):  
Language Processing ◽  
Domain Adaptation ◽  
Recognition Task ◽  
Data Set ◽  
Fine Grained ◽  
Cross Domain ◽  
Cue Recognition ◽  
Annotation Costs ◽  
Key Steps ◽  
Lexical Cues

Uncertainty is an important linguistic phenomenon that is relevant in various Natural Language Processing applications, in diverse genres from medical to community generated, newswire or scientific discourse, and domains from science to humanities. The semantic uncertainty of a proposition can be identified in most cases by using a finite dictionary (i.e., lexical cues) and the key steps of uncertainty detection in an application include the steps of locating the (genre- and domain-specific) lexical cues, disambiguating them, and linking them with the units of interest for the particular application (e.g., identified events in information extraction). In this study, we focus on the genre and domain differences of the context-dependent semantic uncertainty cue recognition task. We introduce a unified subcategorization of semantic uncertainty as different domain applications can apply different uncertainty categories. Based on this categorization, we normalized the annotation of three corpora and present results with a state-of-the-art uncertainty cue recognition model for four fine-grained categories of semantic uncertainty. Our results reveal the domain and genre dependence of the problem; nevertheless, we also show that even a distant source domain data set can contribute to the recognition and disambiguation of uncertainty cues, efficiently reducing the annotation costs needed to cover a new domain. Thus, the unified subcategorization and domain adaptation for training the models offer an efficient solution for cross-domain and cross-genre semantic uncertainty recognition.

scholarly journals Ciphertext-policy attribute-based encryption with hidden sensitive policy from keyword search techniques in smart city

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Fei Meng ◽  
Leixiao Cheng ◽  
Mingqiang Wang
Keyword(s):  
Smart City ◽  
Keyword Search ◽  
Sensitive Information ◽  
Security Model ◽  
Computational Overhead ◽  
Diffie Hellman ◽  
Attribute Based Encryption ◽  
Iot Devices ◽  
Ciphertext Policy ◽  
Access Policies

AbstractCountless data generated in Smart city may contain private and sensitive information and should be protected from unauthorized users. The data can be encrypted by Attribute-based encryption (CP-ABE), which allows encrypter to specify access policies in the ciphertext. But, traditional CP-ABE schemes are limited because of two shortages: the access policy is public i.e., privacy exposed; the decryption time is linear with the complexity of policy, i.e., huge computational overheads. In this work, we introduce a novel method to protect the privacy of CP-ABE scheme by keyword search (KS) techniques. In detail, we define a new security model called chosen sensitive policy security: two access policies embedded in the ciphertext, one is public and the other is sensitive and hidden. If user's attributes don't satisfy the public policy, he/she cannot get any information (attribute name and its values) of the hidden one. Previous CP-ABE schemes with hidden policy only work on the “AND-gate” access structure or their ciphertext size or decryption time maybe super-polynomial. Our scheme is more expressive and compact. Since, IoT devices spread all over the smart city, so the computational overhead of encryption and decryption can be shifted to third parties. Therefore, our scheme is more applicable to resource-constrained users. We prove our scheme to be selective secure under the decisional bilinear Diffie-Hellman (DBDH) assumption.

An Effective Model for Consumer Need Prediction Using Big Data Analytics

2021 ◽  
Author(s):  
Yihao Tian
Keyword(s):  
Big Data ◽  
Consumer Behavior ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Customer Behavior ◽  
Data Set ◽  
Customer Data ◽  
Demand Prediction ◽  
Management Ratio ◽  
The Veil

Big data is an unstructured data set with a considerable volume, coming from various sources such as the internet, business organizations, etc., in various formats. Predicting consumer behavior is a core responsibility for most dealers. Market research can show consumer intentions; it can be a big order for a best-designed research project to penetrate the veil, protecting real customer motivations from closer scrutiny. Customer behavior usually focuses on customer data mining, and each model is structured at one stage to answer one query. Customer behavior prediction is a complex and unpredictable challenge. In this paper, advanced mathematical and big data analytical (BDA) methods to predict customer behavior. Predictive behavior analytics can provide modern marketers with multiple insights to optimize efforts in their strategies. This model goes beyond analyzing historical evidence and making the most knowledgeable assumptions about what will happen in the future using mathematical. Because the method is complex, it is quite straightforward for most customers. As a result, most consumer behavior models, so many variables that produce predictions that are usually quite accurate using big data. This paper attempts to develop a model of association rule mining to predict customers’ behavior, improve accuracy, and derive major consumer data patterns. The finding recommended BDA method improves Big data analytics usability in the organization (98.2%), risk management ratio (96.2%), operational cost (97.1%), customer feedback ratio (98.5%), and demand prediction ratio (95.2%).

Matching Returning Donors to Projects on Philanthropic Crowdfunding Platforms

2021 ◽  
Author(s):  
Yicheng Song ◽  
Zhuoxin Li ◽  
Nachiketa Sahoo
Keyword(s):  
Unique Feature ◽  
Econometric Model ◽  
Current System ◽  
Personalized Recommendation ◽  
Data Set ◽  
Proposed Model ◽  
K 12 ◽  
Structural Econometric Model ◽  
Structural Econometric ◽  
Data Period

We propose an approach to match returning donors to fundraising campaigns on philanthropic crowdfunding platforms. It is based on a structural econometric model of utility-maximizing donors who can derive both altruistic (from the welfare of others) and egoistic (from personal motivations) utilities from donating—a unique feature of philanthropic giving. We estimate our model using a comprehensive data set from DonorsChoose.org—the largest crowdfunding platform for K–12 education. We find that the proposed model more accurately identifies the projects that donors would like to donate to on their return in a future period, and how much they would donate, than popular personalized recommendation approaches in the literature. From the estimated model, we find that primarily egoistic factors motivate over two-thirds of the donations, but, over the course of the fundraising campaign, both motivations play a symbiotic role: egoistic motivations drive the funding in the early stages of a campaign when the viability of the project is still unclear, whereas altruistic motivations help reach the funding goal in the later stages. Finally, we design a recommendation policy using the proposed model to maximize the total funding each week considering the needs of all projects and the heterogeneous budgets and preferences of donors. We estimate that over the last 14 weeks of the data period, such a policy would have raised 2.5% more donation, provided 9% more funding to the projects by allocating them to more viable projects, funded 17% more projects, and provided 15% more utility to the donors from the donations than the current system. Counterintuitively, we find that the policy that maximizes total funding each week leads to higher utility for the donors over time than a policy that maximizes donors’ total utility each week. The reason is that the funding-maximizing policy focuses donations on more viable projects, leading to more funded projects, and, ultimately, higher realized donors’ utility. This paper was accepted by Kartik Hosanagar, information systems.

scholarly journals Attribute Revocable Attribute-Based Encryption with Forward Secrecy for Fine-Grained Access Control of Shared Data

2017 ◽  
Vol E100.D (10) ◽  
pp. 2432-2439
Author(s):  
Yoshiaki SHIRAISHI ◽  
Kenta NOMURA ◽  
Masami MOHRI ◽  
Takeru NARUSE ◽  
Masakatu MORII
Keyword(s):  
Access Control ◽  
Forward Secrecy ◽  
Fine Grained ◽  
Shared Data ◽  
Attribute Based Encryption

scholarly journals Ciphertext-Policy Attribute-based Encryption with Hidden Sensitive Policy for Recruitment in Smart City

2020 ◽  
Author(s):  
Fei Meng ◽  
Leixiao Cheng ◽  
Mingqiang Wang
Keyword(s):  
Smart City ◽  
End Users ◽  
The Other ◽  
Sensitive Information ◽  
Security Model ◽  
Access Policy ◽  
Computational Overhead ◽  
Attribute Based Encryption ◽  
Ciphertext Policy ◽  
Access Policies

Abstract Smart city, as a promising technical tendency, greatly facilitates citizens and generates innumerable data, some of which is very private and sensitive. To protect data from unauthorized users, ciphertext-policy attribute-based encryption (CP-ABE) enables data owner to specify an access policy on encrypted data. However, There are two drawbacks in traditional CP-ABE schemes. On the one hand, the access policy is revealed in the ciphertext so that sensitive information contained in the policy is exposed to anyone who obtains the ciphertext. For example, both the plaintext and access policy of an encrypted recruitment may reveal the company's future development plan. On the other hand, the decryption time scales linearly with the complexity of the access, which makes it unsuitable for resource-limited end users. In this paper, we propose a CP-ABE scheme with hidden sensitive policy for recruitment in smart city. Specifically, we introduce a new security model chosen sensitive policy security: two access policies embedded in the ciphertext, one is public and the other is sensitive and fully hidden, only if user's attributes satisfy the public policy, it's possible for him/her to learn about the hidden policy, otherwise he/she cannot get any information (attribute name and its values) of it. When the user satisfies both access policies, he/she can obtain and decrypt the ciphertext. Compared with other CP-ABE schemes, our scheme supports a more expressive access policy, since the access policy of their schemes only work on the ``AND-gate'' structure. In addition, intelligent devices spread all over the smart city, so partial computational overhead of encryption of our scheme can be outsourced to these devices as fog nodes, while most part overhead in the decryption process is outsourced to the cloud. Therefore, our scheme is more applicable to end users with resource-constrained mobile devices. We prove our scheme to be selective secure under the decisional bilinear Diffie-Hellman (DBDH) assumption.

scholarly journals A New Algorithm for Sketch-Based Fashion Image Retrieval Based on Cross-Domain Transformation

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Haopeng Lei ◽  
Simin Chen ◽  
Mingwen Wang ◽  
Xiangjian He ◽  
Wenjing Jia ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Online Shopping ◽  
Unsolved Problem ◽  
Correct Match ◽  
Retrieval Accuracy ◽  
Fine Grained ◽  
Cross Domain ◽  
Domain Transformation ◽  
Domain Similarity ◽  
Natural Way

Due to the rise of e-commerce platforms, online shopping has become a trend. However, the current mainstream retrieval methods are still limited to using text or exemplar images as input. For huge commodity databases, it remains a long-standing unsolved problem for users to find the interested products quickly. Different from the traditional text-based and exemplar-based image retrieval techniques, sketch-based image retrieval (SBIR) provides a more intuitive and natural way for users to specify their search need. Due to the large cross-domain discrepancy between the free-hand sketch and fashion images, retrieving fashion images by sketches is a significantly challenging task. In this work, we propose a new algorithm for sketch-based fashion image retrieval based on cross-domain transformation. In our approach, the sketch and photo are first transformed into the same domain. Then, the sketch domain similarity and the photo domain similarity are calculated, respectively, and fused to improve the retrieval accuracy of fashion images. Moreover, the existing fashion image datasets mostly contain photos only and rarely contain the sketch-photo pairs. Thus, we contribute a fine-grained sketch-based fashion image retrieval dataset, which includes 36,074 sketch-photo pairs. Specifically, when retrieving on our Fashion Image dataset, the accuracy of our model ranks the correct match at the top-1 which is 96.6%, 92.1%, 91.0%, and 90.5% for clothes, pants, skirts, and shoes, respectively. Extensive experiments conducted on our dataset and two fine-grained instance-level datasets, i.e., QMUL-shoes and QMUL-chairs, show that our model has achieved a better performance than other existing methods.

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