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 IoT Data Privacy Protection Scheme based on Blockchain

2021 ◽  
Vol 769 (4) ◽  
pp. 042034
Author(s):  
Yue Wu ◽  
Liangtu Song ◽  
Lei Liu ◽  
Qijin Wang
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Protection Scheme ◽  
Data Privacy Protection

A federated interpretable scorecard and its application in credit scoring

2021 ◽  
pp. 2142009
Author(s):  
Fanglan Zheng ◽  
Erihe ◽  
Kun Li ◽  
Jiang Tian ◽  
Xiaojia Xiang
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Credit Scoring ◽  
Training Session ◽  
Model Performance ◽  
Parameter Tuning ◽  
Kolmogorov Smirnov ◽  
Data Privacy Protection ◽  
Tuning Process ◽  
Positive Coefficients

In this paper, we propose a vertical federated learning (VFL) structure for logistic regression with bounded constraint for the traditional scorecard, namely FL-LRBC. Under the premise of data privacy protection, FL-LRBC enables multiple agencies to jointly obtain an optimized scorecard model in a single training session. It leads to the formation of scorecard model with positive coefficients to guarantee its desirable characteristics (e.g., interpretability and robustness), while the time-consuming parameter-tuning process can be avoided. Moreover, model performance in terms of both AUC and the Kolmogorov–Smirnov (KS) statistics is significantly improved by FL-LRBC, due to the feature enrichment in our algorithm architecture. Currently, FL-LRBC has already been applied to credit business in a China nation-wide financial holdings group.

Is There a Better Option than the Data Transfer Model to Protect Data Privacy?

2019 ◽  
Vol 42 (2) ◽  
Author(s):  
Alan Toy ◽  
Gehan Gunasekara
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Data Transfer ◽  
Business Environment ◽  
Transfer Model ◽  
Privacy Rights ◽  
Choice Of Law ◽  
Privacy Laws ◽  
Data Privacy Protection ◽  
Extraterritorial Jurisdiction

The data transfer model and the accountability model, which are the dominant models for protecting the data privacy rights of citizens, have begun to present significant difficulties in regulating the online and increasingly transnational business environment. Global organisations take advantage of forum selection clauses and choice of law clauses and attention is diverted toward the data transfer model and the accountability model as a means of data privacy protection but it is impossible to have confidence that the data privacy rights of citizens are adequately protected given well known revelations regarding surveillance and the rise of technologies such as cloud computing. But forum selection and choice of law clauses no longer have the force they once seemed to have and this opens the possibility that extraterritorial jurisdiction may provide a supplementary conceptual basis for championing data privacy in the globalised context of the Internet. This article examines the current basis for extraterritorial application of data privacy laws and suggests a test for increasing their relevance.

A Markov Prediction-Based Privacy Protection Scheme for Continuous Query

2019 ◽  
Vol 28 (09) ◽  
pp. 1950147
Author(s):  
Lei Zhang ◽  
Jing Li ◽  
Songtao Yang ◽  
Yi Liu ◽  
Xu Zhang ◽  
...  
Keyword(s):  
Privacy Protection ◽  
Location Privacy ◽  
Mathematical Proof ◽  
Security Analysis ◽  
Continuous Query ◽  
The Real ◽  
Protection Scheme ◽  
Whole Process ◽  
Prediction Scheme ◽  
Location Privacy Protection

The query probability of a location which the user utilizes to request location-based service (LBS) can be used as background knowledge to infer the real location, and then the adversary may invade the privacy of this user. In order to cope with this type of attack, several algorithms had provided query probability anonymity for location privacy protection. However, these algorithms are all efficient just for snapshot query, and simply applying them in the continuous query may bring hazards. Especially that, continuous anonymous locations which provide query probability anonymity in continuous anonymity are incapable of being linked into anonymous trajectories, and then the adversary can identify the real trajectory as well as the real location of each query. In this paper, the query probability anonymity and anonymous locations linkable are considered simultaneously, then based on the Markov prediction, we provide an anonymous location prediction scheme. This scheme can cope with the shortage of the existing algorithms of query probability anonymity in continuous anonymity locations difficult to be linked, and provide query probability anonymity service for the whole process of continuous query, so this scheme can be used to resist the attack of both of statistical attack as well as the infer attack of the linkable. At last, in order to demonstrate the capability of privacy protection in continuous query and the efficiency of algorithm execution, this paper utilizes the security analysis and experimental evaluation to further confirm the performance, and then the process of mathematical proof as well as experimental results are shown.

Dealing with Data Privacy Protection

2003 ◽  
Author(s):  
Fritz Grupe ◽  
William Kuechler ◽  
Scott Sweeney
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Data Privacy Protection

scholarly journals Privacy-Protection Scheme of a Credit-Investigation System Based on Blockchain

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1657
Author(s):  
Ke Yuan ◽  
Yingjie Yan ◽  
Tong Xiao ◽  
Wenchao Zhang ◽  
Sufang Zhou ◽  
...  
Keyword(s):  
Privacy Protection ◽  
Service Providers ◽  
Security Analysis ◽  
Cloud Service ◽  
Identity Authentication ◽  
Protection Scheme ◽  
Blockchain Technology ◽  
Searchable Symmetric Encryption ◽  
Verification Time ◽  
Identity Privacy

In response to the rapid growth of credit-investigation data, data redundancy among credit-investigation agencies, privacy leakages of credit-investigation data subjects, and data security risks have been reported. This study proposes a privacy-protection scheme for a credit-investigation system based on blockchain technology, which realizes the secure sharing of credit-investigation data among multiple entities such as credit-investigation users, credit-investigation agencies, and cloud service providers. This scheme is based on blockchain technology to solve the problem of islanding of credit-investigation data and is based on zero-knowledge-proof technology, which works by submitting a proof to the smart contract to achieve anonymous identity authentication, ensuring that the identity privacy of credit-investigation users is not disclosed; this scheme is also based on searchable-symmetric-encryption technology to realize the retrieval of the ciphertext of the credit-investigation data. A security analysis showed that this scheme guarantees the confidentiality, the availability, the tamper-proofability, and the ciphertext searchability of credit-investigation data, as well as the fairness and anonymity of identity authentication in the credit-investigation data query. An efficiency analysis showed that, compared with similar identity-authentication schemes, the proof key of this scheme is smaller, and the verification time is shorter. Compared with similar ciphertext-retrieval schemes, the time for this scheme to generate indexes and trapdoors and return search results is significantly shorter.

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