Academic literature on the topic 'Blowfish Privacy'

Policy-aware differential privacy (DP) frameworks such as Blowfish privacy enable more accurate query answers than standard DP. In this work, we build the first policy-aware DP system for interactive data exploration, BlowfishDB, that aims to (i) provide bounded and flexible privacy guarantees to the data curators of sensitive data and (ii) support accurate and efficient data exploration by data analysts. However, the specification and processing of customized privacy policies incur additional performance cost, especially for datasets with a large domain. To address this challenge, we propose dynamic Blowfish privacy which allows for the dynamic generation of smaller privacy policies and their data representations at query time. BlowfishDB ensures same levels of accuracy and privacy as one would get working on the static privacy policy. In this demonstration of BlowfishDB, we show how a data curator can fine-tune privacy policies for a sensitive dataset and how a data analyst can retrieve accuracy-bounded query answers efficiently without being a privacy expert.

In this article, we present a privacy-preserving technique for user-centric multi-release graphs. Our technique consists of sequentially releasing anonymized versions of these graphs under Blowfish Privacy. To do so, we introduce a graph model that is augmented with a time dimension and sampled at discrete time steps. We show that the direct application of state-of-the-art privacy-preserving Differential Private techniques is weak against background knowledge attacker models. We present different scenarios where randomizing separate releases independently is vulnerable to correlation attacks. Our method is inspired by Differential Privacy (DP) and its extension Blowfish Privacy (BP). To validate it, we show its effectiveness as well as its utility by experimental simulations.

Over a few years, there is rapid increase of exchange of data over the net has brought data confidentiality and its privacy to the fore front. Data confidentiality can be achieved by implementing cryptography algorithms during transmission of data which confirms that data remains secure and protected over an insecure network channel. In order to ensure data confidentiality and privacy, cryptography service encryption is used which makes data in unreadable form while the reverse process rearranges data in readable form and known as decryption. All encryption algorithms are intended to provide confidentiality to data, but their performance varies depending on many variables such as key size, type, number of rounds, complexity and data size used. In addition, although some encryption algorithms outperform others, they have been found to be prone to particular attacks. This paper reviews and summarizes the various common hybrid cascaded n-tier encryption models. Additionally, this paper compares and analyzes the performance of common hybrid cascaded 2-tier and 3-tier encryption models obtained during simulation based on encryption/decryption time, avalanche effect and throughput. The models compared with AES are 2-tier models (AES-TWOFISH, AES-BLOWFISH, TWOFISH-AES, BLOWFISH-AES, AES-SERPENT and SERPENT-TWOFISH) and 3-tier models (DES-BLOWFISH-AES, AES-TWOFISH-SERPENT and SERPENT-TWOFISH-AES). The hybrid cascaded model like AES-TWOFISH, AES-BLOWFISH and SERPENT-TWOFISH-AES are better hybrid models with respect to throughput and avalanche effect.

Abstract. Security is the study of encryption and decryption, data hiding, potential attacks, and performance evaluation. Many algorithms perform this purpose. Blowfish is a symmetric block cipher that uses the Feistel network. Although several works employed the Blowfish algorithm for the security of the cloud, there is still no article that lists previous studies. Cloud computing is the transmission of computer services such as servers, storage, databases, networking, software, analytics, and intelligence through the Internet ("the cloud") in order to provide faster innovation, more flexible resources, and cost savings. The most common issue with cloud computing is information security, privacy, confidentiality, and how the cloud provider ensures these services. This paper includes a survey of most previous works that were concerned with using the Blowfish algorithm in achieving cloud security

Abstract: The proposed of this paper is for the security needs of the cloud data center. Blowfish is used to convert encrypt file cuts, takes the minimum amount of time, and has the extreme throughput for encryption and decryption from other compatible algorithms. The idea of spreading and mixing meets the principle of information security. The hybrid approach makes the remote servers more secure when deployed in a cloud environment and, thus helps cloud providers gain more trust from their users. Used for data safety and privacy issues, the basic challenge of separating sensitive data and access control has been met. Cryptography techniques translate original data into an unreadable format. The encryption technique is divided into private key encryption and public-key encryption. Here we will use AES 128-algorithm, DES, RC, and LSB encryption techniques. This technique uses keys to translate data into an unreadable format. Therefore only authorized persons can access data from the cloud server. Ciphertext data is visible to all. Keywords: Cloud, AES, RSA, Blowfish algorithms, Encryption/Decryption Techniques.

Abstract: Nowadays a huge number of organizations use the cloud for storing Big data. And some of the sectors have sensitive data, for example, Military, Agencies, Colleges, Industries, etc. The data can be retrieved when the user requests it. And others can also access the data. Cloud computing provides a lot of features with affordable prices and knowledge accessibility by using the Internet. Security is the main concern in the cloud computing environment as clients store secret information with cloud providers, but sometimes these providers may not be trustful. Splitting data in a safe approach while protecting data from an untrusted cloud is still a demanding topic. In this paper we ensure the right approach for data security and privacy, using Blowfish, and RSA/SRNN algorithms. Keywords: Cloud Computing, Data storage, RSA/SRNN algorithm, Blowfish algorithm, Data storage.

Abstract Over the last decade, differential privacy (DP) has emerged as the gold standard of a rigorous and provable privacy framework. However, there are very few practical guidelines on how to apply differential privacy in practice, and a key challenge is how to set an appropriate value for the privacy parameter ɛ. In this work, we employ a statistical tool called hypothesis testing for discovering useful and interpretable guidelines for the state-of-the-art privacy-preserving frameworks. We formalize and implement hypothesis testing in terms of an adversary’s capability to infer mutually exclusive sensitive information about the input data (such as whether an individual has participated or not) from the output of the privacy-preserving mechanism. We quantify the success of the hypothesis testing using the precision- recall-relation, which provides an interpretable and natural guideline for practitioners and researchers on selecting ɛ. Our key results include a quantitative analysis of how hypothesis testing can guide the choice of the privacy parameter ɛ in an interpretable manner for a differentially private mechanism and its variants. Importantly, our findings show that an adversary’s auxiliary information - in the form of prior distribution of the database and correlation across records and time - indeed influences the proper choice of ɛ. Finally, we also show how the perspective of hypothesis testing can provide useful insights on the relationships among a broad range of privacy frameworks including differential privacy, Pufferfish privacy, Blowfish privacy, dependent differential privacy, inferential privacy, membership privacy and mutual-information based differential privacy.

Nowadays, information storing in third party storage is increased. Outsourcing the data to other storage device or servers which may questioned to the secure environment. However, sensitive data like medical information should need an privacy when it is stored in cloud storage. In this paper, a secure keyword search which provide the resultant data in a encrypted form where the end user can decrypt using the key given to them. It uses the Blowfish to encrypt the data and it also supports the data owner to delete or modify the content of their document. It also ensure accurate relevance score calculation between encrypted index and query vectors.

Nowadays, terabytes of digital data are generated and sent online every second. However, securing this extent of information has always been a challenging task. Cryptography is a fundamental method for securing data, as it makes data unintelligible for attackers, offering privacy to authorized clients. Different cryptographic algorithms have different speeds and costs that make them suitable for different applications. For instance, banking applications need outrageous security amenities, as they utilize superior algorithms having greater requirements, while gaming applications focus more on speed and cost reduction. Consequently, cryptographic algorithms are chosen based on a client's prerequisites. This study compared DES, AES, Blowfish, and RSA, examining their speed, cost, and performance, and discussed their adequacy for use in wireless sensor networks and peer-to-peer communication.

Les données numériques jouent un rôle crucial dans notre vie quotidienne en communiquant, en enregistrant des informations, en exprimant nos pensées et nos opinions et en capturant nos moments précieux sous forme d'images et de vidéos numériques. Les données numériques présentent d'énormes avantages dans tous les aspects de la vie moderne, mais constituent également une menace pour notre vie privée. Dans cette thèse, nous considérons trois types de données numériques en ligne générées par les utilisateurs des médias sociaux et les clients du commerce électronique : les graphiques, les transactions et les images. Les graphiques sont des enregistrements des interactions entre les utilisateurs qui aident les entreprises à comprendre qui sont les utilisateurs influents dans leur environnement. Les photos postées sur les réseaux sociaux sont une source importante de données qui nécessitent des efforts d'extraction. Les ensembles de données transactionnelles représentent les opérations qui ont eu lieu sur les services de commerce électronique.Nous nous appuyons sur une technique de préservation de la vie privée appelée Differential Privacy (DP) et sa généralisation Blowfish Privacy (BP) pour proposer plusieurs solutions permettant aux propriétaires de données de bénéficier de leurs ensembles de données sans risque de violation de la vie privée pouvant entraîner des problèmes juridiques. Ces techniques sont basées sur l'idée de récupérer l'existence ou la non-existence de tout élément dans l'ensemble de données (tuple, ligne, bord, nœud, image, vecteur, ...) en ajoutant respectivement un petit bruit sur la sortie pour fournir un bon équilibre entre intimité et utilité.Dans le premier cas d'utilisation, nous nous concentrons sur les graphes en proposant trois mécanismes différents pour protéger les données personnelles des utilisateurs avant d'analyser les jeux de données. Pour le premier mécanisme, nous présentons un scénario pour protéger les connexions entre les utilisateurs avec une nouvelle approche où les utilisateurs ont des privilèges différents : les utilisateurs VIP ont besoin d'un niveau de confidentialité plus élevé que les utilisateurs standard. Le scénario du deuxième mécanisme est centré sur la protection d'un groupe de personnes (sous-graphes) au lieu de nœuds ou d'arêtes dans un type de graphes plus avancé appelé graphes dynamiques où les nœuds et les arêtes peuvent changer à chaque intervalle de temps. Dans le troisième scénario, nous continuons à nous concentrer sur les graphiques dynamiques, mais cette fois, les adversaires sont plus agressifs que les deux derniers scénarios car ils plantent de faux comptes dans les graphiques dynamiques pour se connecter à des utilisateurs honnêtes et essayer de révéler leurs nœuds représentatifs dans le graphique.Dans le deuxième cas d'utilisation, nous contribuons dans le domaine des données transactionnelles en présentant un mécanisme existant appelé Safe Grouping. Il repose sur le regroupement des tuples de manière à masquer les corrélations entre eux que l'adversaire pourrait utiliser pour violer la vie privée des utilisateurs. D'un autre côté, ces corrélations sont importantes pour les propriétaires de données dans l'analyse des données pour comprendre qui pourrait être intéressé par des produits, biens ou services similaires. Pour cette raison, nous proposons un nouveau mécanisme qui expose ces corrélations dans de tels ensembles de données, et nous prouvons que le niveau de confidentialité est similaire au niveau fourni par Safe Grouping.Le troisième cas d'usage concerne les images postées par les utilisateurs sur les réseaux sociaux. Nous proposons un mécanisme de préservation de la confidentialité qui permet aux propriétaires des données de classer les éléments des photos sans révéler d'informations sensibles. Nous présentons un scénario d'extraction des sentiments sur les visages en interdisant aux adversaires de reconnaître l'identité des personnes
Digital data is playing crucial role in our daily life in communicating, saving information, expressing our thoughts and opinions and capturing our precious moments as digital pictures and videos. Digital data has enormous benefits in all the aspects of modern life but forms also a threat to our privacy. In this thesis, we consider three types of online digital data generated by users of social media and e-commerce customers: graphs, transactional, and images. The graphs are records of the interactions between users that help the companies understand who are the influential users in their surroundings. The photos posted on social networks are an important source of data that need efforts to extract. The transactional datasets represent the operations that occurred on e-commerce services.We rely on a privacy-preserving technique called Differential Privacy (DP) and its generalization Blowfish Privacy (BP) to propose several solutions for the data owners to benefit from their datasets without the risk of privacy breach that could lead to legal issues. These techniques are based on the idea of recovering the existence or non-existence of any element in the dataset (tuple, row, edge, node, image, vector, ...) by adding respectively small noise on the output to provide a good balance between privacy and utility.In the first use case, we focus on the graphs by proposing three different mechanisms to protect the users' personal data before analyzing the datasets. For the first mechanism, we present a scenario to protect the connections between users (the edges in the graph) with a new approach where the users have different privileges: the VIP users need a higher level of privacy than standard users. The scenario for the second mechanism is centered on protecting a group of people (subgraphs) instead of nodes or edges in a more advanced type of graphs called dynamic graphs where the nodes and the edges might change in each time interval. In the third scenario, we keep focusing on dynamic graphs, but this time the adversaries are more aggressive than the past two scenarios as they are planting fake accounts in the dynamic graphs to connect to honest users and try to reveal their representative nodes in the graph. In the second use case, we contribute in the domain of transactional data by presenting an existed mechanism called Safe Grouping. It relies on grouping the tuples in such a way that hides the correlations between them that the adversary could use to breach the privacy of the users. On the other side, these correlations are important for the data owners in analyzing the data to understand who might be interested in similar products, goods or services. For this reason, we propose a new mechanism that exposes these correlations in such datasets, and we prove that the level of privacy is similar to the level provided by Safe Grouping.The third use-case concerns the images posted by users on social networks. We propose a privacy-preserving mechanism that allows the data owners to classify the elements in the photos without revealing sensitive information. We present a scenario of extracting the sentiments on the faces with forbidding the adversaries from recognizing the identity of the persons. For each use-case, we present the results of the experiments that prove that our algorithms can provide a good balance between privacy and utility and that they outperform existing solutions at least in one of these two concepts

In this project you can find a study about different encryption algorithms, which are use to safeguard the information on messages over the network. We have developed a client-server application which will send information through the network which has to be secured. There are two kinds of encryption algorithms, the symmetric and the asymmetric key algorithms. Both were used to establish the communication, the asymmetric algorithm (RSA) is used to set up a symmetric key and then, all the communication process is done only with the symmetric algorithm (Blowfish).
En este proyecto encontraras un estudio sobre diferentes algoritmos de encriptación, que son usados para salvaguardar la información en mensajes por la red. Además hemos desarrollado una aplicación cliente-servidor que enviara información a través de la red de forma segura. Hay dos tipos de algoritmos de encriptación, los simétricos y los asimétricos. Ambos tipos de algoritmos son utilizados para establecer la comunicación, el asimétrico (RSA) es utilizado para establecer la clave del simétrico y a partir de entonces se utilizara exclusivamente el algoritmo simétrico (Blowfish).