scholarly journals Secure Selections on Encrypted Multi-writer Streams

2022 ◽  
Vol 25 (1) ◽  
pp. 1-33
Author(s):  
Angelo Massimo Perillo ◽  
Giuseppe Persiano ◽  
Alberto Trombetta
Keyword(s):  
Efficient Solutions ◽  
Direct Access ◽  
Security Model ◽  
Sensitive Data ◽  
Encrypted Data ◽  
The Public ◽  
Data Owner ◽  
Hard Problems ◽  
Pairing Based Cryptography ◽  
Access Policies

Performing searches over encrypted data is a very current and active area. Several efficient solutions have been provided for the single-writer scenario in which all sensitive data originate with one party (the Data Owner ) that encrypts and uploads the data to a public repository. Subsequently, the Data Owner accesses the encrypted data through a Query Processor , which has direct access to the public encrypted repository. Motivated by the recent trend in pervasive data collection, we depart from this model and consider a multi-writer scenario in which the data originate with several and mutually untrusted parties, the Data Sources . In this new scenario, the Data Owner provides public parameters so that each Data Source can add encrypted items to the public encrypted stream; moreover, the Data Owner keeps some related secret information needed to generate tokens so that different Query Sources can decrypt different subsets of the encrypted stream, as specified by corresponding access policies. We propose security model for this problem that we call Secure Selective Stream ( SSS ) and give a secure construction for it based on hard problems in Pairing-Based Cryptography. The cryptographic core of our construction is a new primitive, Amortized Orthogonality Encryption , that is crucial for the efficiency of the proposed implementation for SSS .

Perancangan Keamanan Pengambilan Kembali Data Menggunakan Enkripsi Simetris pada Struktur ORDBMS

Respati ◽  
2019 ◽  
Vol 14 (1) ◽  
Author(s):  
M. Fairul Filza
Keyword(s):  
Data Security ◽  
Data Integrity ◽  
Database Security ◽  
Symmetric Encryption ◽  
Core Component ◽  
Sensitive Data ◽  
Computing Systems ◽  
Encrypted Data ◽  
The Public ◽  
The Creation

INTISARITeknologi basis data adalah komponen inti dari banyak sistem komputasi. Basis data memungkinkan data yang akan disimpan dan berbagi secara elektronik. Begitu pula kebutuhan untuk memastikan integritas data dan keamanan data dari akses yang tidak diinginkan. Keamanan basis data dibuktikan dengan peningkatan jumlah kekhawatiran dan insiden kehilangan atau pelanggaran dilaporkan terhadap data yang sensitif. Penelitian ini membahas secara teknis pengamanan dalam pengambilan kembali data pada lapisan datasource dengan enkripsi simetris. Hasil dari penilitian ini adalah pembuatan virtual dan mengkamuflase tabel sehingga ketika diakses oleh penguna publik akan menampilkan rekaman data yang terenkripsi. ABSTRACTData base technology is a core component of many computing systems. The database allows data to be stored and shared electronically. Similarly, the need to ensure data integrity and data security from unwanted access. Database security is evidenced by the increasing number of concerns and incidents of loss or breach is reported against sensitive data. This study discusses the technical safeguards in taking back the data on the layer of the datasource with symmetric encryption. The result of this was the creation of a virtual and camouflage tables when accessed by users so that the public will display the encrypted data records.Keyword —  Cryptography, Data Security, Aes, Postgesql, Python, RDBMS

scholarly journals Logistic Regression on Homomorphic Encrypted Data at Scale

2019 ◽  
Vol 33 ◽  
pp. 9466-9471 ◽  
Author(s):  
Kyoohyung Han ◽  
Seungwan Hong ◽  
Jung Hee Cheon ◽  
Daejun Park
Keyword(s):  
Logistic Regression ◽  
Data Privacy ◽  
Homomorphic Encryption ◽  
Training Data ◽  
Sensitive Data ◽  
Encrypted Data ◽  
The Public ◽  
Private Data ◽  
First Time ◽  
Practical Feasibility

Machine learning on (homomorphic) encrypted data is a cryptographic method for analyzing private and/or sensitive data while keeping privacy. In the training phase, it takes as input an encrypted training data and outputs an encrypted model without ever decrypting. In the prediction phase, it uses the encrypted model to predict results on new encrypted data. In each phase, no decryption key is needed, and thus the data privacy is ultimately guaranteed. It has many applications in various areas such as finance, education, genomics, and medical field that have sensitive private data. While several studies have been reported on the prediction phase, few studies have been conducted on the training phase.In this paper, we present an efficient algorithm for logistic regression on homomorphic encrypted data, and evaluate our algorithm on real financial data consisting of 422,108 samples over 200 features. Our experiment shows that an encrypted model with a sufficient Kolmogorov Smirnow statistic value can be obtained in ∼17 hours in a single machine. We also evaluate our algorithm on the public MNIST dataset, and it takes ∼2 hours to learn an encrypted model with 96.4% accuracy. Considering the inefficiency of homomorphic encryption, our result is encouraging and demonstrates the practical feasibility of the logistic regression training on large encrypted data, for the first time to the best of our knowledge.

scholarly journals Access to Sensitive Data: Satisfying Objectives Rather than Constraints

2014 ◽  
Vol 30 (3) ◽  
pp. 533-545 ◽  
Author(s):  
Felix Ritchie
Keyword(s):  
Cost Benefit ◽  
Data Access ◽  
Legal Responsibility ◽  
Sensitive Data ◽  
The Public ◽  
Data Owner ◽  
Level Data ◽  
Good Nature ◽  
The Uk

The argument for access to sensitive unit-level data produced within government is usually framed in terms of risk and the legal responsibility to maintain confidentiality. This article argues that the framing of the question may restrict the set of possibilities; a more effective perspective starts from the data owner’s principles and user needs. Within this principlesbased framework, the role of law changes: It becomes an ‘enabling technology’, helping to define the solution but playing no role in setting the objectives. This shift in perspective has a number of consequences. The perception of ‘costs’ and ‘benefits’ is reversed. Law and established practice are distinguished and appropriately placed within a cost-benefit framework. The subjectivity and uncertainty in risk assessments is made explicit. Overall, all other things being equal, the expectation is that a move towards objective-based planning increases data access and improves risk assessment. This alternative perspective also addresses the problem of the public-good nature of research outputs. It encourages the data owner to engage with users and build a case for data access taking account of the wider needs of society. The UK data access regime is used as the primary example of the arguments in this article

International Conference "Ecology. Human. Society"

2017 ◽  
Author(s):  
Daniel Benatov
Keyword(s):  
Educational Process ◽  
Direct Access ◽  
Human Society ◽  
Public Authorities ◽  
State Institutions ◽  
The Public ◽  
Market Participants ◽  
New Information ◽  
Training Courses ◽  
Process Element

Our conference is the first project of Student Science Association, which was restored in our University in 1998. The main peculiarity of the conference is the student organizing committee. The conference was attended by representatives of Russia, Belarus, Sweden, Poland, Bulgaria, Armenia, Azerbaijan, Czech Republic, Lithuania, Latvia, Georgia, Iran, not mentioning hundreds of Ukrainian participants. We’re happy with the fact that our conference allows students to discover new information, which they wouldn’t find in training courses manuals; contrariwise businesses and organizations can get direct access to young and qualified staff. We believe that events like our conference are useful for the young scientists and also for the public authorities and businesses. Conference "Ecology. Human. Society "is a part of feedback between universities and market participants. The conference has overgrown limits of being simple educational process element. Today, it is a serious recruiting resource for state institutions and businesses - an important part of a mutually beneficial dialogue.

scholarly journals Privacy-Preserving Deep Neural Network Methods: Computational and Perceptual Methods—An Overview

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1367
Author(s):  
Raghida El El Saj ◽  
Ehsan Sedgh Sedgh Gooya ◽  
Ayman Alfalou ◽  
Mohamad Khalil
Keyword(s):  
Neural Networks ◽  
Classification Accuracy ◽  
Deep Neural Networks ◽  
Privacy Preserving ◽  
Sensitive Data ◽  
Encrypted Data ◽  
High Classification Accuracy ◽  
Hide Information ◽  
Network Methods ◽  
Cloud Environments

Privacy-preserving deep neural networks have become essential and have attracted the attention of many researchers due to the need to maintain the privacy and the confidentiality of personal and sensitive data. The importance of privacy-preserving networks has increased with the widespread use of neural networks as a service in unsecured cloud environments. Different methods have been proposed and developed to solve the privacy-preserving problem using deep neural networks on encrypted data. In this article, we reviewed some of the most relevant and well-known computational and perceptual image encryption methods. These methods as well as their results have been presented, compared, and the conditions of their use, the durability and robustness of some of them against attacks, have been discussed. Some of the mentioned methods have demonstrated an ability to hide information and make it difficult for adversaries to retrieve it while maintaining high classification accuracy. Based on the obtained results, it was suggested to develop and use some of the cited privacy-preserving methods in applications other than classification.

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.

Computing Blindfolded on Data Homomorphically Encrypted under Multiple Keys: A Survey

2022 ◽  
Vol 54 (9) ◽  
pp. 1-37
Author(s):  
Asma Aloufi ◽  
Peizhao Hu ◽  
Yongsoo Song ◽  
Kristin Lauter
Keyword(s):  
Homomorphic Encryption ◽  
Lessons Learned ◽  
Secret Key ◽  
Sensitive Data ◽  
Encrypted Data ◽  
Outsourced Computation ◽  
Secret Keys ◽  
Comprehensive Survey ◽  
Multiple Keys ◽  
Cryptographic Techniques

With capability of performing computations on encrypted data without needing the secret key, homomorphic encryption (HE) is a promising cryptographic technique that makes outsourced computations secure and privacy-preserving. A decade after Gentry’s breakthrough discovery of how we might support arbitrary computations on encrypted data, many studies followed and improved various aspects of HE, such as faster bootstrapping and ciphertext packing. However, the topic of how to support secure computations on ciphertexts encrypted under multiple keys does not receive enough attention. This capability is crucial in many application scenarios where data owners want to engage in joint computations and are preferred to protect their sensitive data under their own secret keys. Enabling this capability is a non-trivial task. In this article, we present a comprehensive survey of the state-of-the-art multi-key techniques and schemes that target different systems and threat models. In particular, we review recent constructions based on Threshold Homomorphic Encryption (ThHE) and Multi-Key Homomorphic Encryption (MKHE). We analyze these cryptographic techniques and schemes based on a new secure outsourced computation model and examine their complexities. We share lessons learned and draw observations for designing better schemes with reduced overheads.

scholarly journals Privacy-Preserving Sorting Algorithms Based on Logistic Map for Clouds

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Hua Dai ◽  
Hui Ren ◽  
Zhiye Chen ◽  
Geng Yang ◽  
Xun Yi
Keyword(s):  
Data Privacy ◽  
Logistic Map ◽  
Security Analysis ◽  
Privacy Preserving ◽  
Service Recommendation ◽  
Sensitive Data ◽  
Encrypted Data ◽  
Sorting Algorithms ◽  
Common Operation ◽  
Cloud Servers

Outsourcing data in clouds is adopted by more and more companies and individuals due to the profits from data sharing and parallel, elastic, and on-demand computing. However, it forces data owners to lose control of their own data, which causes privacy-preserving problems on sensitive data. Sorting is a common operation in many areas, such as machine learning, service recommendation, and data query. It is a challenge to implement privacy-preserving sorting over encrypted data without leaking privacy of sensitive data. In this paper, we propose privacy-preserving sorting algorithms which are on the basis of the logistic map. Secure comparable codes are constructed by logistic map functions, which can be utilized to compare the corresponding encrypted data items even without knowing their plaintext values. Data owners firstly encrypt their data and generate the corresponding comparable codes and then outsource them to clouds. Cloud servers are capable of sorting the outsourced encrypted data in accordance with their corresponding comparable codes by the proposed privacy-preserving sorting algorithms. Security analysis and experimental results show that the proposed algorithms can protect data privacy, while providing efficient sorting on encrypted data.

scholarly journals A Secure Ciphertext Retrieval Scheme against Insider KGAs for Mobile Devices in Cloud Storage

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Run Xie ◽  
Chanlian He ◽  
Dongqing Xie ◽  
Chongzhi Gao ◽  
Xiaojun Zhang
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Data Privacy ◽  
Security Analysis ◽  
Data Retrieval ◽  
Sensitive Data ◽  
Encrypted Data ◽  
Security Issues ◽  
Efficiency Comparison ◽  
Cloud Servers

With the advent of cloud computing, data privacy has become one of critical security issues and attracted much attention as more and more mobile devices are relying on the services in cloud. To protect data privacy, users usually encrypt their sensitive data before uploading to cloud servers, which renders the data utilization to be difficult. The ciphertext retrieval is able to realize utilization over encrypted data and searchable public key encryption is an effective way in the construction of encrypted data retrieval. However, the previous related works have not paid much attention to the design of ciphertext retrieval schemes that are secure against inside keyword-guessing attacks (KGAs). In this paper, we first construct a new architecture to resist inside KGAs. Moreover we present an efficient ciphertext retrieval instance with a designated tester (dCRKS) based on the architecture. This instance is secure under the inside KGAs. Finally, security analysis and efficiency comparison show that the proposal is effective for the retrieval of encrypted data in cloud computing.

scholarly journals Protecting Sensitive Data with Secure Data Enclaves

2021 ◽  
Author(s):  
Mark Howison ◽  
Mintaka Angell ◽  
Michael Hicklen ◽  
Justine S. Hastings
Keyword(s):  
Data Sharing ◽  
Data Use ◽  
Data Access ◽  
Cloud Services ◽  
Reference Architecture ◽  
Sensitive Data ◽  
Technical Requirements ◽  
Data Owner ◽  
Secure Data ◽  
Amazon Web Services

A Secure Data Enclave is a system that allows data owners to control data access and ensure data security while facilitating approved uses of data by other parties. This model of data use offers additional protections and technical controls for the data owner compared to the more commonly used approach of transferring data from the owner to another party through a data sharing agreement. Under the data use model, the data owner retains full transparency and auditing over the other party’s access, which can be difficult to achieve in practice with even the best legal instrument for data sharing. We describe the key technical requirements for a Secure Data Enclave and provide a reference architecture for its implementation on the Amazon Web Services platform using managed cloud services.

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