scholarly journals CONFIDENTIALITY PROTECTIVE USER PROFILE MATCHING IN PUBLIC NETWORKS

2021 ◽  
pp. 152-158
Author(s):  
Maddala Mounika ◽  
K. Tulasi Krishna Kumar Nainar
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Online Dating ◽  
Homomorphic Encryption ◽  
User Profile ◽  
User Profiles ◽  
User Query ◽  
Profile Matching ◽  
Multiple Servers ◽  
Elgamal Encryption

We consider a scenario where a user queries a user profile database, maintained by a social networking service provider, to identify users whose profiles match the profile specified by the querying user. A typical example of this application is online dating. Most recently, an online dating website, Ashley Madison, was hacked, which results in disclosure of a large number of dating user profiles. This data breach has urged researchers to explore practical privacy protection for user profiles in a social network. Here, we propose a privacy-preserving solution for profile matching in social networks by using multiple servers. Our solution is built on homomorphic encryption and allows a user to find out matching users with the help of multiple servers without revealing to anyone the query and the queried user profiles in clear. Our solution achieves user profile privacy and user query privacy as long as at least one of the multiple servers is honest. Our experiments demonstrate that our solution is practical. KEY WORDS: User profile matching, data privacy protection, ElGamal encryption.

scholarly journals Deep Learning Data Privacy Protection Based on Homomorphic Encryption in AIoT

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yichuan Wang ◽  
Xiaolong Liang ◽  
Xinhong Hei ◽  
Wenjiang Ji ◽  
Lei Zhu
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Privacy Protection ◽  
Data Privacy ◽  
Homomorphic Encryption ◽  
Accuracy Rate ◽  
Learning Network ◽  
Shared Data ◽  
Data Privacy Protection ◽  
Deep Learning Network

With the rapid development of 5G technology, its high bandwidth, high reliability, low delay, and large connection characteristics have opened up a broader application field of IoT. Moreover, AIoT (Artificial Intelligence Internet of Things) has become the new development direction of IoT. Through deep learning of real-time data provided by the Internet of Things, AI can judge user habits more accurately, make devices behave in line with user expectations, and become more intelligent, thus improving product user experience. However, in the process, there is a lot of data interaction between the edge and the cloud. Given that the shared data contain a large amount of private information, preserving information security on the shared data is an important issue that cannot be neglected. In this paper, we combine deep learning with homomorphic encryption algorithm and design a deep learning network model based on secure multiparty computing (MPC). In the whole process, we realize that the cloud only owns the encryption samples of users, and users do not own any parameters or structural information related to the model. In the experimental part, we input the encrypted Mnist and Cifar-10 datasets into the model for testing, and the results show that the classification accuracy rate of the encrypted Mnist can reach 99.21%, which is very close to the result under plaintext. The classification accuracy rate of encrypted Cifar-10 can reach 91.35%, slightly lower than the test result in plaintext and better than the existing deep learning network model that can realize data privacy protection.

Cross-Platform User Profile Matching in Online Social Networks

2013 ◽  
Vol 380-384 ◽  
pp. 1955-1958 ◽  
Author(s):  
Dong Liu ◽  
Quan Yuan Wu
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Similarity Measures ◽  
User Profile ◽  
Future Research ◽  
User Profiles ◽  
String Similarity ◽  
Profile Matching ◽  
Cross Platform ◽  
String Similarity Measures

Nowadays, it is common that people have several identities in different online social networks where their identities information is stored as user profiles. Matching cross-platform user profiles becomes a spotlight in the future research. In the paper, we propose a profile matching framework. Depending on the format of each field, different string similarity measures are adopted. Meanwhile, each fields importance is considered. At last, we evaluate the effectiveness of our proposed methods by experiments.

scholarly journals A Privacy Protection Scheme for IoT Big Data Based on Time and Frequency Limitation

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Lei Zhang ◽  
Yu Huo ◽  
Qiang Ge ◽  
Yuxiang Ma ◽  
Qiqi Liu ◽  
...  
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Privacy Protection ◽  
Social Life ◽  
Data Privacy ◽  
Homomorphic Encryption ◽  
The Internet ◽  
Protection Scheme ◽  
The Internet Of Things ◽  
Frequency Limitation

Various applications of the Internet of Things assisted by deep learning such as autonomous driving and smart furniture have gradually penetrated people’s social life. These applications not only provide people with great convenience but also promote the progress and development of society. However, how to ensure that the important personal privacy information in the big data of the Internet of Things will not be leaked when it is stored and shared on the cloud is a challenging issue. The main challenges include (1) the changes in access rights caused by the flow of manufacturers or company personnel while sharing and (2) the lack of limitation on time and frequency. We propose a data privacy protection scheme based on time and decryption frequency limitation that can be applied in the Internet of Things. Legitimate users can obtain the original data, while users without a homomorphic encryption key can perform operation training on the homomorphic ciphertext. On the one hand, this scheme does not affect the training of the neural network model, on the other hand, it improves the confidentiality of data. Besides that, this scheme introduces a secure two-party agreement to improve security while generating keys. While revoking, each attribute is specified for the validity period in advance. Once the validity period expires, the attribute will be revoked. By using storage lists and setting tokens to limit the number of user accesses, it effectively solves the problem of data leakage that may be caused by multiple accesses in a long time. The theoretical analysis demonstrates that the proposed scheme can not only ensure safety but also improve efficiency.

scholarly journals A Regulatable Data Privacy Protection Scheme for Energy Transactions Based on Consortium Blockchain

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yufeng Li ◽  
Yuling Chen ◽  
Tao Li ◽  
Xiaojun Ren
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Security Analysis ◽  
Transaction Data ◽  
Protection Scheme ◽  
Data Mining Algorithms ◽  
Private Data ◽  
Elgamal Encryption ◽  
Data Privacy Protection ◽  
Mining Algorithms

In the blockchain-based energy transaction scenario, the decentralization and transparency of the ledger will cause the users’ transaction details to be disclosed to all participants. Attackers can use data mining algorithms to obtain and analyze users’ private data, which will lead to the disclosure of transaction information. Simultaneously, it is also necessary for regulatory authorities to implement effective supervision of private data. Therefore, we propose a supervisable energy transaction data privacy protection scheme, which aims to trade off the supervision of energy transaction data by the supervisory authority and the privacy protection of transaction data. First, the concealment of the transaction amount is realized by Pedersen commitment and Bulletproof range proof. Next, the combination of ElGamal encryption and zero-knowledge proof technology ensures the authenticity of audit tickets, which allows regulators to achieve reliable supervision of the transaction privacy data without opening the commitment. Finally, the multibase decomposition method is used to improve the decryption efficiency of the supervisor. Experiments and security analysis show that the scheme can well satisfy transaction privacy and auditability.

scholarly journals D2D Data Privacy Protection Mechanism Based on Reliability and Homomorphic Encryption

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 51140-51150 ◽  
Author(s):  
Biao Jin ◽  
Dongshuo Jiang ◽  
Jinbo Xiong ◽  
Lei Chen ◽  
Qi Li
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Homomorphic Encryption ◽  
Protection Mechanism ◽  
Data Privacy Protection

Student Privacy and the Law in the Internet Age

2019 ◽  
Author(s):  
Leah Plunkett ◽  
Urs Gasser ◽  
Sandra Cortesi
Keyword(s):  
Learning Environments ◽  
School Environment ◽  
Privacy Protection ◽  
Data Privacy ◽  
Digital Technologies ◽  
The United States ◽  
Legal Framework ◽  
Privacy Law ◽  
Student Privacy ◽  
High Level

New types of digital technologies and new ways of using them are heavily impacting young people’s learning environments and creating intense pressure points on the “pre-digital” framework of student privacy. This chapter offers a high-level mapping of the federal legal landscape in the United States created by the “big three” federal privacy statutes—the Family Educational Rights and Privacy Act (FERPA), the Children’s Online Privacy Protection Act (COPPA), and the Protection of Pupil Rights Amendment (PPRA)—in the context of student privacy and the ongoing digital transformation of formal learning environments (“schools”). Fissures are emerging around key student privacy issues such as: what are the key data privacy risk factors as digital technologies are adopted in learning environments; which decision makers are best positioned to determine whether, when, why, and with whom students’ data should be shared outside the school environment; what types of data may be unregulated by privacy law and what additional safeguards might be required; and what role privacy law and ethics serve as we seek to bolster related values, such as equity, agency, and autonomy, to support youth and their pathways. These and similar intersections at which the current federal legal framework is ambiguous or inadequate pose challenges for key stakeholders. This chapter proposes that a “blended” governance approach, which draws from technology-based, market-based, and human-centered privacy protection and empowerment mechanisms and seeks to bolster legal safeguards that need to be strengthen in parallel, offers an essential toolkit to find creative, nimble, and effective multistakeholder solutions.

Semantic Representation of a Geo-Social User Profile for a Personalised Information Retrieval

2021 ◽  
pp. 2150044
Author(s):  
Tahar Rafa ◽  
Samir Kechid
Keyword(s):  
Information Retrieval ◽  
Information Search ◽  
Semantic Representation ◽  
User Profile ◽  
Search Process ◽  
Search System ◽  
User Interactions ◽  
User Interests ◽  
Situational Contexts ◽  
User Query

The user-centred information retrieval needs to introduce semantics into the user modelling for a meaningful representation of user interests. The semantic representation of the user interests helps to improve the identification of the user’s future cognitive needs. In this paper, we present a semantic-based approach for a personalised information retrieval. This approach is based on the design and the exploitation of a user profile to represent the user and his interests. In this user profile, we combine an ontological semantics issued from WordNet ontology, and a personal semantics issued from the different user interactions with the search system and with his social and situational contexts of his previous searches. The personal semantics considers the co-occurrence relations between relevant components of the user profile as semantic links. The user profile is used to improve two important phases of the information search process: (i) expansion of the initial user query and (ii) adaptation of the search results to the user interests.

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