scholarly journals Secure Sum Computation using Threshold Encryption for Semi-Ideal Model

2020 ◽  
Vol 8 (5) ◽  
pp. 4406-4409
Keyword(s):  
Function Evaluation ◽  
Correct Result ◽  
Ideal Model ◽  
Sensitive Data ◽  
Real Model ◽  
Private Data ◽  
Common Task ◽  
The Subject ◽  
Threshold Encryption ◽  
Common Function

The need of preserving privacy of data arises when multiple parties work together on some common task. In this scenario each of the parties has to provide its sensitive data for a common function evaluation. But, the parties may be worried about the misuse of the data. Here comes the subject of Secure Multiparty Computation (SMC). The area where multiple cooperating parties jointly evaluate a common function of their data while preserving the privacy and getting the correct result is SMC. Here, we devise a new model and algorithm to compute sum of private data of mutually distrustful cooperating parties. We coin the term Semi-Ideal Model as it is hybrid of Ideal model and real model. The computation is secure on insecure network as well. Keyword

scholarly journals Confidential Health Care Data Exchange Between Multiple Entities in Cloud using Block Chain

2019 ◽  
Vol 9 (2) ◽  
pp. 2566-2573
Keyword(s):  
Health Care ◽  
Data Exchange ◽  
Health Care Organizations ◽  
Main Concept ◽  
Sensitive Data ◽  
Privacy And Security ◽  
Health Care Data ◽  
Private Data ◽  
Peer Network ◽  
Block Chain

Data in the cloud is leading to the more interest for cyber attackers. These days’ attackers are concentrating more on Health care data. Through data mining performed on health care data Industries are making Business out of it. These changes are affecting the treatment process for many people so careful data processing is required. Breaking these data security leads to many consequences for health care organizations. After braking security computation of private data can be performed. By data storing and running of computation on a sensitive data can be possible by decentralization through peer to peer network. Instead of using the centralized architecture by decentralization the attacks can be reduced. Different security algorithms have been considered. For decentralization we are using block chain technology. Privacy, security and integrity can be achieved by this block chain technology. Many solutions have been discussed to assure the privacy and security for Health care organizations somehow failed to address this problem. Many cryptographic functions can be used for attaining privacy of data. Pseudonymity is the main concept we can use to preserve the health care means preserving data by disclosing true identity legally.

scholarly journals Keadilan Hukum Jeremy Bentham dan Relevansinya bagi Praktik Hukum Kontemporer

Humaniora ◽  
2012 ◽  
Vol 3 (1) ◽  
pp. 299
Author(s):  
Frederikus Fios
Keyword(s):  
Global Politics ◽  
Jeremy Bentham ◽  
Ideal Model ◽  
The Core ◽  
The Subject ◽  
Core Idea ◽  
The Universe ◽  
Universe Of Discourse ◽  
British Philosopher ◽  
The Ideal

Fair punishment for a condemned has been long debated in the universe of discourse of law and global politics. The debate on the philosophical level was no less lively. Many schools of thought philosophy question, investigate, reflect and assess systematically the ideal model for the subject just punishment in violation of the law. One of the interesting and urgent legal thought Jeremy Bentham, a British philosopher renowned trying to provide a solution in the middle of the debate was the doctrine or theory of utilitarianism. The core idea is that the fair punishment should be a concern for happiness of a condemned itself, and not just for revenge. Bentham thought has relevance in several dimensions such as dimensions of humanism, moral and utility.  

Providing Restricted Access to Mental Health Archives within Government Archives: The Subject Stakeholder

2021 ◽  
Vol 84 (1) ◽  
pp. 165-188
Author(s):  
Patricia Galloway
Keyword(s):  
State Hospital ◽  
Psychiatric Hospitals ◽  
Central State ◽  
Sensitive Data ◽  
Community Archives ◽  
Stakeholder Groups ◽  
The People ◽  
State Psychiatric ◽  
The Subject ◽  
State Archives

ABSTRACT Since 2010, the author has been part of the Central State Hospital (CSH) Digital Library and Archives Project to digitize records from the first state psychiatric hospital for African Americans, founded in 1870 in Virginia at the pleadings of the Freedman's Bureau and run by the state since then.1 Many of the records of this hospital not yet accessioned by the Library of Virginia have now been digitized, and this project is working on a set of tools for lawful access, including one that can be used for automated redaction to protect sensitive data while responding to the needs of different stakeholder groups. Project participants were especially concerned about understanding the communities that have grown up around state-run psychiatric hospitals, as the project was done at the request of the hospital. The proposed plan is to work with the Central State Hospital and the Library of Virginia to provide the project materials to both. The records that were chosen to be digitized included the minutes of the people who first ran the hospital as well as the registers kept on the patients, which differ over time.2 In the past ten to fifteen years, professional discussion about community archives has responded to communities' desires to build their own archives so that they can be treated fairly, especially with reference to records created about them and kept by others, including records found in state archives.

Privacy Preserving Machine Learning and Deep Learning Techniques

2020 ◽  
pp. 222-235
Author(s):  
Divya Asok ◽  
Chitra P. ◽  
Bharathiraja Muthurajan
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Privacy ◽  
Differential Privacy ◽  
Digital Data ◽  
Sensitive Data ◽  
Security Issues ◽  
Private Data ◽  
Learning Techniques ◽  
Machine Learning Applications

In the past years, the usage of internet and quantity of digital data generated by large organizations, firms, and governments have paved the way for the researchers to focus on security issues of private data. This collected data is usually related to a definite necessity. For example, in the medical field, health record systems are used for the exchange of medical data. In addition to services based on users' current location, many potential services rely on users' location history or their spatial-temporal provenance. However, most of the collected data contain data identifying individual which is sensitive. With the increase of machine learning applications around every corner of the society, it could significantly contribute to the preservation of privacy of both individuals and institutions. This chapter gives a wider perspective on the current literature on privacy ML and deep learning techniques, along with the non-cryptographic differential privacy approach for ensuring sensitive data privacy.

scholarly journals Program Analysis For Database Injections

2017 ◽  
Vol 16 (6) ◽  
pp. 6977-6986
Author(s):  
Chelsea Ramsingh ◽  
Paolina Centonze
Keyword(s):  
Open Source ◽  
Private Information ◽  
Program Analysis ◽  
Query Language ◽  
Cyber Attacks ◽  
Test Cases ◽  
Sensitive Data ◽  
Sql Injection ◽  
Testing Tools ◽  
Private Data

Today businesses all around the world use databases in many different ways to store sensitive data. It is important that the data stored stay safe and does not get into the wrong hands. To perform data management in a database, the language SQL (Structured Query Language) can be used. It is extremely crucial to prevent these databases from being attacked to ensure the security of the users’ sensitive and private data. This journal will focus on the most common way hackers exploit data from databases through SQL injection, and it presents dynamic and static code testing to find and prevent these SQL cyber attacks by comparing two testing tools. It will also present a comparative analysis and static/dynamic code testing of two SQL injection detection tools. Burp Suite and Vega will be used to identify possible flaws in test cases dealing with users’ sensitive and private information. Currently, there are no comparisons of these two open-source tools to quantify the number of flaws these two tools are able to detect. Also, there are no detailed papers found fully testing the open-source Burp Suite and Vega for SQL Injection. These two open-source tools are commonly used but have not been tested enough. A static analyzer detecting SQL Injection will be used to test and compare the results of the dynamic analyzer. In addition, this paper will suggest techniques and methods to ensure the security of sensitive data from SQL injection. The prevention of SQL injection is imperative and it is crucial to secure the sensitive data from potential hackers who want to exploit it.

scholarly journals A Protocol for Checking Equality of Data Using Hash in Ideal Model of Secure Multiparty Computation

2021 ◽  
Vol 23 (3) ◽  
Author(s):  
Rashid Sheikh ◽  
◽  
Rashid Sheikh ◽  
Durgesh Kumar Mishra ◽  
Meghna Dubey ◽  
...  
Keyword(s):  
Hash Function ◽  
Simple Algorithm ◽  
Secure Multiparty Computation ◽  
Third Party ◽  
Function Evaluation ◽  
Multiparty Computation ◽  
Ideal Model ◽  
Trusted Third Party ◽  
Secure Function Evaluation ◽  
The Ideal

The ideal Secure Multiparty Computation (SMC) model deploys a Trusted Third Party (TTP) which assists in secure function evaluation. The participating joint parties give input to the TTP which provide the results to the participating parties. The equality check problem in multiple party cases can be solved by simple architecture and a simple algorithm. In our proposed protocol Equality Hash Checkin ideal model, we use a secure hash function. All the parties interested to check equality of their data supply hash of their data to the TTP which then compared all hash values for equality. It declares the result to the parties.

scholarly journals “Restoring the Kinship”: the Novel The Adolescent as a Prologue to the Dialogue Between Nikolay Fedorov and Fyodor Dostoevsky

2021 ◽  
pp. 58-87
Author(s):  
Anastasia G. Gacheva
Keyword(s):  
The Novel ◽  
Fyodor Dostoevsky ◽  
Main Work ◽  
Common Task ◽  
The World ◽  
The Subject ◽  
The Common

The article is an attempt to read the novel The Adolescent in the light of the spiritual and creative dialogue between the philosopher of the common task Nikolay Fedorov and Fyodor Dostoevsky. Although The Adolescent was written and published three years before Fedorov’s student N. Peterson presented his teacher’s ideas to the writer in the article “What should a people’s school be?”, the novel can be considered as a prologue to the topic that eventually became the subject of Fedorov’s main work The question of brotherhood or kinship, about the causes of the non-fraternal, unrelated, i.e. non-peaceful, state of the world, and about the means to restore kinship. The plot of the novel is interpreted in the article through the prism of Fedorov’s themes of non-kinship and the restoration of universal kinship, the idea of returning the hearts of sons to their fathers and the fathers’ ones to their children. It is shown how the theme of “family as the practical beginning of love” is expressed in the novel.

scholarly journals Private Function Evaluation Using Intel’s SGX

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Omar Abou Selo ◽  
Maan Haj Rachid ◽  
Abdullatif Shikfa ◽  
Yongge Wang ◽  
Qutaibah Malluhi
Keyword(s):  
Homomorphic Encryption ◽  
The Other ◽  
Third Party ◽  
Function Evaluation ◽  
Side Channel ◽  
Side Channel Attacks ◽  
Practical Solution ◽  
Running Time ◽  
First Case ◽  
Private Data

Private Function Evaluation (PFE) is the problem of evaluating one party’s private data using a private function owned by another party. Existing solutions for PFE are based on universal circuits evaluated in secure multiparty computations or on hiding the circuit’s topology and the gate’s functionality through additive homomorphic encryption. These solutions, however, are not efficient enough for practical use; hence there is a need for more efficient techniques. This work looks at utilizing the Intel Software Guard Extensions platform (SGX) to provide a more practical solution for PFE where the privacy of the data and the function are both preserved. Notably, our solution carefully avoids the pitfalls of side-channel attacks on SGX. We present solutions for two different scenarios: the first is when the function’s owner has an SGX-enabled device and the other is when a third party (or one of the data owners) has the SGX capability. Our results show a clear expected advantage in terms of running time for the first case over the second. Investigating the slowdown in the second case leads to the garbling time which constitutes more than 60% of the consumed time. Both solutions clearly outperform FairplayPF in our tests.

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.

Swedish gärna and German gern(e) and their English correspondences

2013 ◽  
Vol 13 (2) ◽  
pp. 238-259
Author(s):  
Karin Aijmer ◽  
Bengt Altenberg
Keyword(s):  
Speech Acts ◽  
Parallel Corpora ◽  
Parallel Corpus ◽  
Contrastive Study ◽  
Wide Range ◽  
Multilingual Corpus ◽  
The Subject ◽  
The Rich ◽  
Linguistic Phenomenon ◽  
Common Function

The Swedish adverb gärna, related to German gern(e), has no obvious equivalent in English. To explore this cross-linguistic phenomenon the English correspondences of gärna are examined on the basis of the English-Swedish Parallel Corpus, a bidirectional translation corpus. The study shows that gärna has a wide range of English correspondences (translations as well as sources), representing a variety of grammatical categories (verb, adjective, adverb, noun, etc). In addition, the English texts contain a large number of omissions and unidentifiable sources (zero). The most common function of gärna is to express willingness or readiness on the part of the subject, but in the absence of a volitional controller it can also indicate a habitual tendency and even convey implications such as reluctance. It is also used in speech acts expressing offers, promises and requests and in responses to such speech acts. To compare the Swedish adverb with its German cognate gern(e) a similar contrastive study of the English correspondences of this adverb was made on the basis of the Oslo Multilingual Corpus. The studies clearly demonstrate the rich multifunctionality of the two adverbs and the advantages of using bidirectional parallel corpora in contrastive research.

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