Analysis of privacy preserving echanisms for outsourced data mining

The trends of data mining on healthcare data for improving medical services have increased because of the electronic healthcare record(EHR) system, which collects a massive amount of data on a daily basis. In the current scenario, hospital maintains its EHR system and stores the detailed information of patients. Data mining for healthcare improvement requires the data from all the EHR systems located at a different location to be stored at the central data mining server. Collection of healthcare data at some untrusted central data mining server raises privacy threats. Healthcare data contains patients’ private information and sharing this information for data mining creates privacy issues. Most of the previous research either focused on k-anonymity technique which causes information loss and decreases data mining accuracy or privacy preserving data mining which is focused on only specific data mining technique. We adopt source anonymous technique as privacy preserving scheme and present a novel scheme for healthcare data collection and mining in this paper. Our scheme collects data from all EHR systems without any information loss and stores at a single central data mining server, also ensuring privacy is preserved. Central data mining server helps to analyze the collected data with different data mining techniques (Association rule mining, Classification, Clustering, etc.) without the involvement of EHR systems. Our scheme is collusion resilient against central data mining server and EHR systems. Theoretical and experimental analysis show the efficiency of our scheme in terms of computation and communication cost. The experimental results using Heart disease dataset show the advantage to EHR systems using the proposed approach in terms of disease prediction accuracy.

In data mining applications, sharing of huge volume of detailed personal data is proved to be beneficial. Different types of data include criminal records, medical history, shopping habits, credit records, etc. These types of data are very much significant for any company and governments for decision making. When analyzing the data, privacy policy may avoid data owners from sharing information. In privacy preserving, data owner must provide a solution for achieving the dual goal of privacy preservation as well as accurate clustering result. Nowadays, privacy issues are challenging concern for the data miners. Privacy preservation is a multitalented task as it ensures the privacy of individuals without trailing the correctness of data mining results. This paper proposes data transformation methods for privacy preserving clustering based on fuzzy in centralized database environment. After experimenting, results proved that hybrid approach gives best results for all the member functions.

In today's competitive scenario in industries, each organization wants huge profit. A Data Warehouse is a place for storage of reservoir of data from the various organizations in the range of hundreds of gigabytes to terabytes in size. During the course of time, large volumes of data are being gathered continuously in the Data Warehouse Server for further mining operations in order to understand the latest trends and patterns in their business organizations which in turn be exploited by them for increasing their business profits. Organizations want to conceal its sensitive data from the Data Mining Server but still have the aim to obtain accurate data mining results. Privacy Preserving Data Mining is one of the important fields in the data security. This paper deals with Object Oriented Modeling of Privacy Preservation framework in which the privacy preserving technique applied at the DataWarehouseServer. Object Oriented Programming is the latest trend in the software industry due to its various features like reusability of software codes, better user interface, enhancing software security by restricting data access, cheaper production, development and maintenance costs and quicker software development. Authors have modeled the entire Privacy Preserving framework through standard UML diagrams such as use case, class diagram, sequence diagram and Activity diagram by using Rational Software Architect tool. This will facilitate the designers and developers to have a better understanding of the functionality of each and every entity associated with the system during analysis and design phase, before actual implementation. This simplifies the complexity of the entire operation and helps its implementation in a time and cost efficient manner.

In view of present advancements in computing, with the development of distributed environment, many problems have to deal with distributed input data where individual data privacy is the most important issue to be addressed, for the concern of data owner by extending the privacy preserving notion to the original learning algorithms. Privacy Preserving Data Mining has become an active research area in addressing various privacy issues while bringing out solutions for them. There has been lot of progress in developing secure algorithms and models, able to preserve privacy using various data mining techniques like association, classification and clustering, where as importance of privacy preserving techniques applied for learning algorithms related to neural networks for mining problems are still in infancy. Our work in this paper focused on preserving privacy of an individual, using self organizing map (SOM) adopted for collaborative clustering of distributed data between multiple parties. We present Privacy Preserving Collaborative Clustering method using SOM (PPCSOM) for Horizontal Data Distribution, which allows multiple parties perform clustering in a collaborative approach using SOM neural network, without revealing their data directly to each other, in order to preserve privacy of all parties. Our simulation results shows that implementation of PPCSOM method achieves comparable accuracy and assured Privacy than the original non privacy preserving SOM algorithm.

Data is increasing drastically day by day specifically due to the usage of social networking websites like Facebook, Twitter, etc. It is very difficult for data owners to store and manipulate such a huge size of data as it incurs more cost of maintaining resources. To overcome this issue, data owners utilize cloud resources to limit the expenses. But as a third party, the cloud can be curious which may lead to the disclosure of personal data. This led researchers to use some techniques to provide privacy in cloud storage used by data owners and this is referred to as privacy-preserving data mining (PPDM). PPDM preserves the privacy of data owners in the cloud, so the private data remains private even after the mining process. This paper focuses on some of the important PPDM techniques like data distortion, encryption, etc. It brings out an extensive survey of privacy-preserving data mining techniques, their benefits and drawbacks, and put forth the open challenges for further research.

Efficient integrity verification of replicated data in cloud computing system

The Finnish Biodiversity Information Facility FinBIF receives, stores and manages biodiversity data mobilised in Finland, and shares the data through its own portal (species.fi) and through Global Biodiversity Information Facility GBIF. FinBIF’s data policy (data policy) embraces the European FAIR data principles (FAIR - Findable, Accessible, Interoperable, Reusable; Wilkinson (2016)) but also incorporates specific restrictions stemming from national legislation, researchers’ needs, and data owners’ requirements. Here, we describe how the necessary, due to various reasons from sensitivity of the data to research embargo, restrictions to openness have been defined and implemented on the policy level and in technical data infrastructure solutions. We hope to contribute to an improvement of data management in the international biodiversity data infrastructures. In Finland, the law prohibits public authorities from distributing occurrence data if this causes increased threat to endangered species. However, neither the definition of ‘endangered species’ nor guidelines for the evaluation of potential risk by openness of data are formulated. To enable mobilisation of datasets containing information on endangered species, FinBIF convened a task force commissioned to set rules on data distribution, which respect the spirit of the law. The task force consisted of representatives of relevant data holding authorities and it consulted a wide group of taxon experts and the species information community. First, a list of species, judged to be among those targeted by the spirit of the law, was created (sensitive species data). Then the rules of restriction were decided on for each of the species. Measures of restriction ranged from complete non-disclosure of data to temporal and spatial restrictions. The identified safeguards concerning the sensitive data management in all use cases led us to create a series of innovative solutions Researchers often wish to restrict the openness of data they have gathered for research purposes These restrictions include embargo periods, limitations on the precision of data and controls on how the data is used. In many cases, however, researchers are willing to allow unrestricted official use of their data in certain cases such as for conservation management or land use planning. In these cases they will often allow storage and restricted use of exact data without an embargo. The same may be true for other data owners, such as nongovernmental organisations (NGOs) or private citizens. To support restrictions to openness, while simultaneously securing mobilisation of valuable datasets, FinBIF applies data sharing contracts including, as a rule, a precondition to share the original data with the public authorities for official use under the Creative Commons 4.0 BY -licence (CC 4.0 BY). The technical solution to enabling the rather complex data policy is that FinBIF stores the collated data in two separate data warehouses: a public one for the distribution of fully open data and temporally and spatially coarsened sensitive data, alongside another containing all data but with restricted access to authorised users. In addition, to allow case-by-case release of restricted data, FinBIF has developed a data request function (Fig. 1). When users of the open data retrieve a dataset using, e.g., taxonomic and spatial filtering, they receive a search result stating whether there are restricted data available based on the filters used. In these cases a user can issue a data request, automatically distributed to all owners of data contained in the collated data batch. Agreeing on the principles about how to apply restrictions to data openness and how to define authoritative use, has not been easy given the lack of precedents. It has required thorough and inclusive consultation with both state administration, conservation practitioners, scientific specialists and lawyers. The two main cultural constraints to overcome have been (1) embracing the FAIR principles of truly ‘as open as possible’ and only ‘as closed as (absolutely) necessary (European Commission 2016); and, perhaps surprisingly, (2) figuring out novel ways to work across different state administrative sectors to share data.

The explosions of new data mining techniques has augmented privacy risks because now it is probable to powerfully coalesce and cross-examine massive data stores, accessible on the web, in the rummage around of earlier unidentified hidden patterns. Consecutively to make a overtly accessible system safe and sound, we must guarantee not only that private sensitive data have been trimmed out, but also to make certain that certain inference channels have been clogged-up. The data and the concealed knowledge in this data should be made secure. Furthermore, the requirement for making our system as open as probable - to the extent that data sensitivity is not jeopardized - asks for diverse techniques that account for the revelation organize of sensitive data. At its nucleus, the value of privacy preserving data mining is plagiaristic not only from its knack to haul out imperative knowledge, but also from its resiliency to molestation. It performs well at needed levels during times of both crisis and normal operations. This task force's central thrust is towards establishing a earth with robust data security, where knowledge users persist to profit from data without compromising the data privacy.The goal of privacy-preserving data mining is to liberate a dataset that researchers can study without being able to identify sensitive information about any individuals in the data (with high probability). One technique for privacy-preserving data mining is to replace the sensitive items by unknown values. For many situations it is safer if the sanitization process consign unknown values as a substitute of fake values. This obscures the susceptible rules, whilst defending the punter of the data commencing false rules. In this study, we modify the blocking algorithms of[1] by proposing a new heuristic in order to reduce the information loss. We put forward an enhanced approach that overcomes the privacy breach problem of existing blocking approaches. Though they have argued that the rules are truly safe from an attack by an adversary, they have not formally proved the safety, which we have proved. We have investigated how probabilistic and information theoretic techniques can be applied to this problem. More complete analysis of the effectiveness of these rule obscuring techniques, and formal study of the problem has been made. Our preliminary domino effect point toward deterministic algorithms for privacy preserving association rules shows potential framework for controlling disclosure of sensitive data and knowledge.

Cloud computing provides facilities of shared computer processing resources and data to computers and other device on demand. System environment developed by using three key entities trusted third party, data owner and user. The concept of shared authority based privacy preserving authentication protocol i.e., SAPA used to develop system to perform shared access in multiple user. Security and privacy issue as well as shared access authority achieved by using access request matching mechanism e.g. authentication, user privacy, user can only access its own data fields. The multiple users want to share data so that purpose re-encryption is used to provide high security for user private data. Privacy preserving data access authority sharing is attractive for multi user collaborative cloud applications.

Privacy preserving rule-based classifier using modified artificial bee colony algorithm

Current cloud computing systems pose serious limitation to protecting users' data confidentiality. Since users' sensitive data is presented in unencrypted forms to remote machines owned and operated by third party service providers, the risks of unauthorized disclosure of the users' sensitive data by service providers may be quite high. There are many techniques for protecting users' data from outside attackers, but currently no effective way is available for protecting users' sensitive data from service providers in cloud computing. In this paper, an approach is presented to protecting the confidentiality of users' data from service providers, and ensures service providers cannot collect users' confidential data while the data is processed and stored in cloud computing systems. Our approach has three major aspects: (1) separating software service providers and infrastructure service providers in cloud computing, (2) hiding information about the owners of data, and (3) data obfuscation. An example to show how our approach can protect the confidentiality of users' data from service providers in cloud computing is given.

A number of privacy preserving techniques have been proposed recently in data mining. In this paper, we provide a review of the state-of-the-art methods for privacy preserving data mining. and discuss methods for randomization, secure multipart computation, and so on. We also make a classification for the privacy preserving data mining technologies, and analyze some works in this field, such as data distortion method for achieving privacy preserving association rule mining. Detailed evaluation criteria of privacy preserving algorithm were illustrated, which include algorithm performance, data utility, and privacy protection degree. Finally, the development of privacy preserving data mining for further directions is given.

The Android operating system dominates the smartphone market. Thus, to service the market, the number of Android applications has risen dramatically. These applications are processing a great amount of sensitive data, which could result in various concerns including data leakage and privacy violations. For example, applications may misuse the sensitive data stored on Android devices and violate the privacy of the user. Therefore, it is essential to maintain user privacy and protect sensitive data from leakage. Static data flow analysis approaches are used for analyzing Android applications to uncover security and privacy issues. However, these approaches frequently generate false alarms, given the different challenges created by Android applications, such as inter-component communication (ICC), reflection, and implicit flow. This work presents the DroidRista approach for conducting static data flow analysis on Android applications to detect sensitive data leakage. DroidRista analyzes ICC, reflection, and implicit flow in Android applications. To evaluate the performance of DroidRista, it was tested on three data sets. The results demonstrate improved performance in terms of detecting data leakage compared to existing static data flow analysis approaches.

With the rapid growth of fifth-generation mobile networks and vehicle to everything communication technologies on Internet of vehicles, vehicular social networks (VSNs) present new research fields for content sharing, data dissemination, and delivery services. Data owners outsource valuable information in the public cloud and share with others. But for security consideration, the owners tend to encrypt the outsourced data and unwilling to share the decryption key. Thus, there is a dilemma: owners will risk privacy leakage by handing over the key while hiding the decryption key, and other users cannot share the data. In this paper, we propose and analyze a selective sharing scheme (S2PD) for vehicular data owners to share their sensitive data with some authorized data users in VSN. S2PD ensures different users acquiring different data with different privilege received from the data owners. In the scheme, the owners upload the privacy data encrypted by key pairs and let the cloud decode the encrypted data to half-decrypted when they need to share the data with some users; the authorized users download the half-decrypted data and decrypt it with a secret key sent by the sharing owners in a secure manner. Then, it protects the data leakage from the curious cloud server and other unauthorized data users. With the increasing data and sharing requests in VSN, compared with other five sharing scheme, experiments proved our scheme is practically efficient.

Federated learning is a promising tool in the Internet-of-Things (IoT) domain for training a machine learning model in a decentralized manner. Specifically, the data owners (e.g., IoT device consumers) keep their raw data and only share their local computation results to train the global model of the model owner (e.g., an IoT service provider). When executing the federated learning task, the data owners contribute their computation and communication resources. In this situation, the data owners have to face privacy issues where attackers may infer data property or recover the raw data based on the shared information. Considering these disadvantages, the data owners will be reluctant to use their data to participate in federated learning without a well-designed incentive mechanism. In this article, we deliberately design an incentive mechanism jointly considering the task expenditure and privacy issue of federated learning. Based on a differentially private federated learning (DPFL) framework that can prevent the privacy leakage of the data owners, we model the contribution as well as the computation, communication, and privacy costs of each data owner. The three types of costs are data owners' private information unknown to the model owner, which thus forms an information asymmetry. To maximize the utility of the model owner under such information asymmetry, we leverage a 3-D contract approach to design the incentive mechanism. The simulation results validate the effectiveness of the proposed incentive mechanism with the DPFL framework compared to other baseline mechanisms.