Principle T6 (Data Governance) of the Code of Practice for Statistics states: Organisations should look after people’s information securely and manage data in ways that are consistent with relevant legislation and serve the public good.” The General Data Protection Regulation (GDPR) places obligations to process
This paper aims at formula-based cloud dataaccess control (FCDAC) in cloud computing. One challenge in this context is to achieve fine-grained and dataconfidentiality, which is not provided by current work. FCDAC, it’s an access policy determined by our MAS architecture, not by the CSPs. It’s also defined as access is granted not based on the rights of the subject associated with a cloud user after authentication, but based on attributes of the cloud user. In this paper we propose secure MAS architecture to achieve this goal. We want to argue in this pa- per that secure MAS architecture is good alternative to fully implement complex programs in distributed envi- ronments such as cloud computing. Our proposed FCDAC based on MAS architecture consists of four layers: interface layer, existing access control layer, proposed FCDAC layer and CDS layer as well as four types of ent- ities of Cloud Service Provider (CSP), cloud users, knowledge base and confidentiality policy roles. In fact, a complete prototype of the application is permanently running on a server provided by AgentCities in a world- wide agent-based network of services. Even though there is still a lot of work to be done in the security field in MAS, this paper tries to show that it is feasible to apply concepts of information security in these systems. Our results in the practical scenario defined formally in this paper, show the Round Trip Time (RTT) for an agent to travel in our system changes more or less linearly over the number of cloud users in the system measured by the times required for an agent to travel around different number of cloud users before and after implementing FCDAC.
request and access system, allowing researchers to simultaneously apply for access to manipulate, extract, and analyze online data from a number of European countries. Challenges such as metadata collection, record linkage, confidentiality protection methods, resource discovery, and software development were researched, documented, and, in some cases, addressed. The FSRDC model, with 29 active data centers around the United States, has successfully addressed the inherent tension between the need to protect the federal statistical system’s confidential data and the recognition of the utility of these valuable microdata in advancing research for the public good. The FSRDC model meets the legal requirements for microdata collected by the U.S. Census Bureau, but is more restrictive than necessary for most data custodians that seek to disseminate data
From time to time, conflicts may arise between the needs of different groups of users, or between the needs of users and the resources available to meet those needs. In such circumstances it may be necessary to apply methods of disclosure control other than those specified in this annex. Such a decision should only be taken after assessment of the options and with referral to the Head of Profession. However, basic confidentiality protection must be maintained at all times.
ABSTRACT: Cloud storage is one of the most promising services in cloud computing. It offers elastic scaling and low-cost data storage. However, the security issues in the cloud are the main concern that hinders the popularity and application of cloud services. The most important issues related to data storage in the cloud are dataconfidentiality, authentication, and regulations on dataaccess. A straightforward solution to protect the dataconfidentiality is to encrypt the data before outsourcing to the untrusted cloud server. A malicious administrator possibly creates an account as a legitimate user and compromises the security of encrypted database in numerous ways. An access control is essential for categorizing the data based on the sensitivity level of the health records. This work proposes the cryptographically Enforced Access control for Securing Electronic medical records in the cloud (CEASE). The CEASE includes three components to ensure the confidentiality of medical data. Initially, it exploits the trusted proxy server and applies the Advanced Encryption Standard (AES) on the health data before uploading it to the cloud server. Secondly, the proxy server applies access control policy on health data in the cloud using a set of attributes which are offered during user registration. The proxy server involves in processing encrypted queries to read the encrypted data from the cloud and also decrypts the data using the attributes before delivering the data to an end user. Finally, it introduces the partial shuffling within a restricted data block that contains the hot health records and thus, it ensures the dataaccess pattern confidentiality without degrading the querying speed. The performance of CEASE technique is evaluated in the Java platform, and the results show that the CEASE significantly protects the confidentiality of critical data in the cloud platform.
Background Medical records are not only a vital tool for the delivery of health care to individual patients but also hold information with signiﬁcant potential for research. However, patient records contain personal information, and some medical details may be particularly sensitive. The Wellcome Trust produced a draft consensus statement for the use of patient data in research as a result of dis- cussions with GPs, researchers and patient groups. The purpose of this document, produced in May 2008, was to provide guidelines for best practice when general practice records are used for research. Method The recommendations made in the con- sensus statement were discussed by academic pri- mary care researchers, National Health Service (NHS) research and development (R and D) department
The Object-Role Model of our UoD is shown in Figure 2. A care provider is uniquely identified with his UZI number and has one of the six identified roles in the hospital’s policy (i.e., medical specialist, specialist in training, nurse, apothecary, paramedic, secretary). A medical specialist is a subtype of care provider having a specialism, belonging to some hospital specialism, who is responsible for the care given to some patient. In practice, the role of treating specialist may be fulfilled by more than one medical specialist, as a specific specialist may not be present at all time. A patient may also have to deal with several specialisms, who may be involved in one or more care questions, possibly overlapping in time. A patient is treated by a certain hospital specialism if there is an open or completed DBC for that specialism. As stated in Section 2, a DBC is created for a patient for each care question, but it may be the case that a DBC has not yet been created for a patient. To model the log that keeps track of patient dataaccess, each entry should at least contain four entries denoting the date, the time, the patient identification number (BSN), and the care provider identification number (UZI). These four entries form a minimal set that is needed to identify the care provider and moment of access for a certain patient record. Each log entry should be unique.
Adequate access control is key to protect the stored data. Access control has traditionally been provided by operating systems or applications restricting access to the information, which typically exposes all the information if the system or application is hacked. A better approach is to protect the information using encryption that only allows decryption by authorized entities. Attribute-Based Encryption (ABE) is one of the most powerful techniques for access control in cloud storage systems. In the past years, quite a few attribute-based access control schemes were proposed, in which the data owners deﬁne the access policies based on the attributes required by the data and encrypt the data based on the access policies. By this way the data owners are able to ensure that only the users meeting the access policies can decrypt the ciphertexts. However, it is difficult to update the policies when these ABE based schemes are applied because the data owners do not store the data in their local systems once they outsource the data into the cloud servers. It is also difficult to verify the legitimacy of the downloaded data as the clouds housing the data are not trustworthy. Besides, the operations of encryption and decryption in ABE have a high computational overhead and incur a large energy consumption.
curious”. That is, all data servers run protocol exactly as specified, but tries to learn as much as possible about the patient data. In addition, assuming that at least one data server is not compromised by the inside attackers. DataAccess security-In the patient access control system, only the person who are authorized can get access to the sensitive patient data. The patient data cannot be disclosed to any data server during the access. Paillier Public-Key Cryptosystem is used by the user (e.g., Doctor) to access the patient data and monitor the patient’s health condition. The user sends the request including the patient’s identity, attribute of the data, the signature of the user on the query, and the certificate of the user to the three data servers through secure channels. The secure channels is used for the user to place his queries because the patient’s personal details in the queries needs to be protected against outside attackers. If the user’s request passes the signature verification and meets the access control policies, then the servers can identify the shares of the data according the patient’s identity and the attribute of the data.
Despite the benefits offered by cloud-based DBMS, many people still have problems. This is most likely due to the various security issues that have yet to be deal with. These security issues mainly focuses that cloud DBMS are hard to monitor since they often move across multiple hardware stacks and / or servers. Security becomes a serious issue with cloud DBMS when there’s multiple virtual machines that might be able to access a database without being noticed or setting off any alerts . In this situation a malicious person could potentially access the sensitive data or cause serious damage to the integral structure of a database, putting the entire system in danger.
providers (organizations providing software-, platform-, or infrastructure-as-a-service via the cloud) and security issues faced by their customers (companies or organizations who host applications or store data on the cloud). The responsibility is shared, however. The provider must ensure that their infrastructure is secure and that their clients’ data and applications are protected, while the user must take measures to fortify their application and use strong passwords and authentication measures. When an organization elects to store data or host applications on the public cloud, it loses its ability to have physical access to the servers hosting its information. As a result, potentially sensitive data is at risk from insider attacks. According to a recent Cloud Security Alliance report, insider attacks are the sixth biggest threat in cloud computing. Therefore, cloud service providers must ensure that thorough background checks are conducted for employees who have physical access to the servers in the data center. Additionally, data centers must be frequently monitored for suspicious activity.
6) Software as a service(SaaS)Databases and application software’s are accessed by the user in SaaS environment. These applications and databases run on virtual computing environment. The service provider is free to distribute the computation among different resources available. The user is able to see this procedure of transfer between machines. It is sometimes known as software on demand. Billing is done according to the pay-per-use basis. Here the server can access applications in client and vice versa.
Personal privacy may be disrupt due to the unapproved access to individual data, the unwanted acknowledgement of one’s sensitive information, the use of private data for desire other than the one for which data has been collected, etc. To deal with the confidentiality problems in data mining, known as privacy preserving data mining (PPDM) has achieved a considerable progress in later years. The intention of PPDM is to conserve knowing information from unwanted disclosure, and until, preserve the advantage of the data. The attention of PPDM is two turn. First, sensitive raw data, such as personal ID card number and cell phone number, not permitted to use directly for mining. Second, sensitive mining results whose exposure will result in privac y disrupt should be rejected.
Software-as-a-Service (SaaS) is a type of cloud computing in which a tenant rents access to a shared, typically web-based application hosted by a provider. Access control for SaaS should enable the tenant to control access to data that are located at the provider side, based on tenant-specific access control policies. Moreover, with the growing adoption of SaaS by large enterprises, access control for SaaS has to integrate with on-premise applications, inherently leading to a federated set-up. However, in the state of the art, the provider completely evaluates all policies, including the tenant policies. This (i) forces the tenant to disclose sensitive access control data and (ii) limits policy evaluation performance by having to fetch this policy-specific data. To address these challenges, we propose to decompose the tenant policies and evaluate the resulting parts near the data they require as much as possible while keeping sensitive tenant data local to the tenant environment. We call this concept policy federation. In this paper, we motivate the need for policy federation using an in-depth case study analysis in the domain of e-health and present a policy federation algorithm based on a widely-applicable attribute-based policy model. Furthermore, we show the impact of policy federation on policy evaluation time using the policies from the case study and a prototype implementation of supporting middleware. As shown, policy federation effectively succeeds in keeping the sensitive tenant data confidential and at the same time improves policy evaluation time in most cases.
In this paper author proposed a scheme, called AMOEBA that provides location privacy by mitigating the location tracking of vehicles, and protects user privacy by providing vehicles with anonymous access to LBS applications and he discussed about the robustness and liability of the proposed scheme, against active attacks on vehicle safety but here author not considered about mobility of vehicles that will incorporate intersection behavior due to traffic signs and the effects of congested streets, combined with map data and with communication traffic models.
Wearable devices are installed with sensors and softwares which collect data and information about the users. This data is later pre-processed to extract essential insights about user. These devices broadly cover fitness, health and entertainment requirements. The pre-requisite from internet of things technology for wearable applications is to be highly energy efficient or ultra-low power and small sized.
Abstract: In today’s era, cloud computing becomes the hottest topic due to its ability to reduce the cost associated with computing. Cloud computing provides the on-demand services like storage, servers, resources etc. to the users without physically acquiring them and the payment is according to pay per use. Since cloud provides the storage, reduces the managing cost and time for an organization to the user but security and confidentiality become one of the biggest obstacles in front of us. The major problem with cloud environment is the number of the user is uploading their data on cloud storage so sometimes due to lack of security, there may be chances of loss of confidentiality. To overcome these obstacles a third party is required to prevent data, data encryption, and integrity and control unauthorized access to data storage to the cloud. Cloud portability becomes critical in the situation of loss of confidentiality. To optimize better results we will review some paper and find the better results to remove the security barriers.
Abstract - Cloud Computing is one of the emerging technologies in Computer Science. Cloud provides various types of services to us. Database Outsourcing is a recent data management paradigm in which the data owner stores the confidential data at the third party service provider’s site. The service provider is responsible for managing and administering the database and allows the data owner and clients to create, update, delete and access the database. There are chances of hampering the security of the data due to untrustworthiness of service provider. So, to secure the data which is outsourced to third party is a great challenge. The major requirements for achieving security in outsourced databases are confidentiality, privacy, integrity, availability. To achieve these requirements various dataconfidentiality mechanisms like fragmentation approach, High-Performance Anonymization Engine approach etc. are available. In this paper, various mechanisms for implementing DataConfidentiality in cloud computing are analyzed along with their usefulness in a great detail.
Data sources, being the physical implementations of information systems are heterogeneous because they are designed by different communities and for different circumstances. This heterogeneity is manifested in terms of data storage formats (XML, Relational, Object Oriented,), languages of queries (XQUERY, SQL, OQL ...), access protocols (HTTP ...) and schema's formalisms of data.