privacy preserving algorithms

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Privacy Preserving Algorithms for Newly Emergent Computing Environments

Privacy Preserving Algorithms for Newly Emergent Computing Environments

Watermarking is a technique to embed specific data in a digital content. The watermarking scheme has enormous diversity. It can be categorized [128, 129, 130] based on the embedding domain (Spatial domain, Transform domain, Feature domain), watermarking host signal (video signal, audio signal, IC design, etc.), availability of original signal during extraction (blind, semi–blind, non–blind) etc. The watermarking technique is advantageous because, to embed the watermark into the original data this technique does not create any separate file to store authentication information. Besides all the advantages of the watermarking technique, any modification on the embedded data can be manipulated easily. Therefore one important requirement of the watermark technique is to make it almost unrecognizable and robust so that watermark cannot be removed or modified by any attack. To make the biometric watermarking technique more secure, encryption based privacy preserving techniques can be applied. Some watermarking techniques (e.g., fragile, robust) becomes invalid or detectable if a slight modification or some image processing operations such as image scaling, cropping, bending is done on the watermarked image. As any individual’s biometric features are unique therefore these biometric features should not be disclosed under any circumstances to any adversary. In our work, we focus on the privacy preserving secure biometric watermarking scheme for authentication purposes. We preserve the privacy of users’ watermarked biometrics using cryptography. To overcome the vulnerability of biometric authentications and to protect the privacy of a user’s watermarked biometrics, we use biometric authentication in conjunction with digital watermarking and cryptography. We embed the fingerprint on the facial image as a watermark to improve the security of user authentication. To achieve the privacy of a user’s biometric traits we encrypt the watermarked biometric before user verification.
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Privacy Preserving Association Rule Mining of
          Mixed Partitioned Model in Distributed Database
          Environment

Privacy Preserving Association Rule Mining of Mixed Partitioned Model in Distributed Database Environment

In 1996, Clifton et al. [3] discussed and presented ideas related to the issue of protecting privacy of individuals in the database. The state of the art in the area of privacy preserving data mining techniques is discussed by the authors in [4] [5]. This paper also describes the different dimensions of preserving data mining techniques such as data distribution, data modification technique, data mining algorithms, data or rule hiding and approaches for privacy preserving data mining techniques. In [6], the authors proposed a framework for evaluating privacy preserving data mining algorithms and based on their frame work one can assess the different features of privacy preserving algorithms according to different evaluation criteria. Evfimievski et al. presented a new framework for preserving privacy association rule mining [7]. In order to find privacy preserving association rule mining in centralized database, a new algorithm is presented in [8] which balancs privacy preserving and knowledge discovery in association rule mining. Gkoulalas Divanis, et al. addressed many issues related to privacy preserving data mining, association rule hiding, classes of association rule hiding methodologies and also rule hiding in classification technique, privacy preserving clustering & sequence hiding [9].
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Privacy Preserving in Data Mining by Normalization

Privacy Preserving in Data Mining by Normalization

plus limited memory space has constrained the traditional methods from obtaining the mining result with accuracy. In view of the above-mentioned issues, studies on Privacy- Preserving Data Stream Mining in recent years have become one of the important issues in the field of data mining. Several privacy preserving algorithms have been proposed and are used nowadays. In this paper, we propose a new method using min-max normalization for preserving data through data mining. In general, min- max normalization is used as a preprocessing step in data mining for transformation of data to a desired range. Our purpose is to use it for preserving privacy through data mining. We use K- means clustering to validate the proposed approach and validate for accuracy.
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Preserving Users’ Location Privacy in Mobile Platforms

Preserving Users’ Location Privacy in Mobile Platforms

introduced in Chapter 3, highlighting how we support our design goals mentioned in 3.3. In short, we implemented PL-Protector as a practical and functional proof-of-concept to test and validate our theoretical model. The design of PL-Protector implements the two location privacy-preserving algorithms, which are defined in the LP-Cache model, as LPPMs that includes: LPPM-1) On-device Location Computation Mechanism, and LPPM-2) Personalised Location Permissions Mechanism. Both of these LPPMs enable robust and efficient source (OS) to sink (3rd party app provider) flow control while sharing the user’s sensitive locations. For instrumenting PL-Protector, we selected Android platform since it is open source. Nonetheless, our results can be extrapolated to other permission-based mobile platforms such as iOS. PL-Protector uses fingerprinting to create a private location database within the device instead of storing it on remote location servers. This minimises the process of wireless AP data collection by the WiFi content distributors or location providers. In addition, PL-Protector controls information disclosure within the generated LBS query (e.g., PoIs and nearest neighbour) since it will be sent to third-party app providers. We ran PL-Protector on a Nexus 6P with Android 6.0 that acts as the platform’s location privacy knob to minimise the risk of major location privacy threats. PL-Protector only requires process isolation and IPC (Inter-Process Communication) services from the underlying OS; thus, minimising the requirements placed on the hardware/OS.
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Privacy and trustworthiness management in moving object environments

Privacy and trustworthiness management in moving object environments

The second challenge tackled in this dissertation is how to achieve privacy preserving location publishing when the total number of trajectories is extremely large. As mentioned earlier, LBS users generate 1.5 EB of data every month, and this number is projected to grow to 15.9 EB per month in 2018 [81]. Last year’s global mobile internet traffic, at 18 EB, was 18 times the size it was in 2000. This increase is attributed to over a half a billion mobile devices being added to mobile networks last year. [81] Much of that data has location and trajectory information that is stored for analysis. Currently, the data limit for database type storage systems is in the order of exabytes [86]. While this is impressive, the amount of information generated from several cities reporting trajectory data will very quickly exceed this limit. In order to handle data of this magnitude, companies rely on hundreds of thousands of computers working in parallel [87]. And even with these resources, processing time can be often very slow due to the need to access several machines at once and storing the data on multiple servers to allow fault tolerance and recovery. With processing times slow enough already, anonymizing the data to protect privacy will make it take even longer. None of the existing location publishing techniques have considered how to deal with big trajectory datasets.
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SLICING: A SECURED DESIGN FOR THE MICRO DATA PUBLICATION

SLICING: A SECURED DESIGN FOR THE MICRO DATA PUBLICATION

The technique of anonymity is considered as a very powerful technique of privacy protection. The design of the internet which is stateless and decentralized is the one which is particularly suitable for anonymous behaviour. Though anonymous actions can ensure privacy they are not supposed to be used as the only source of ensuring the privacy as they also allow harmful activities such as slander, spamming and other harmful activities without a fear of reprisal. Security is the main concern that says that one should be able of detecting and catching the individuals who are conducting illegal behaviour such as conspiring for terrorist acts, hacking and conducting a fraud activity. Lawful needs for privacy should be allowed and at the same time the ability for conducting the harmful anonymous behaviour without repercussions and responsibility by saying the name of privacyshould not.
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A Case Study on Mining Security Issues & Remedies in Privacy Preservation

A Case Study on Mining Security Issues & Remedies in Privacy Preservation

Abstract: The development in data mining technology brings serious threat to the individual information. The objective of privacy preserving data mining (PPDM) is to safeguard the sensitive information contained in the data. The unwanted disclosure of the sensitive information may happen during the process of data mining results. In this study we identify four different types of users involved in mining application i.e. data source provider, data receiver, data explorer and determiner decision maker. We differentiate each type of user’s responsibilities and privacy concerns with respect to sensitive information. We’d like to provide useful insights into the study of privacy preserving data mining. This paper presents a comprehensive noise addition technique for protecting individual privacy in a data set used for classification, while maintaining the data quality. We add noise to all attributes, both numerical and categorical, and both to class and non-class, in such a way so that the original patterns are preserved in a perturbed data set. Our technique is also capable of incorporating previously proposed noise addition techniques that maintain the statistical parameters of the data set, including correlations among attributes. Thus the perturbed data set may be used not only for classification but also for statistical analysis.
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Privacy Preserving in Association Rule Mining

Privacy Preserving in Association Rule Mining

transaction to the server, the client takes each item and with probability replaces it by a new item not originally present in this transaction. Let us call this process uniform randomization. estimate true (nonrandomized) support of an item set is nontrivial even for uniform randomization. Randomized support of, say, a 3-itemset depends not only on its true support, but also on the supports of its subsets. certainly, it is much more likely that only one or two of the items are inserted by chance than all three. So, almost all \false" occurrences of the item set are due to (and depend on) high subset supports. This requires estimating the supports of all subsets consecutively. For large values of p, most of the items in most randomized transactions will be \false", so we seem to have obtained a logical privacy security. Also, if there are enough clients and transactions, then frequent item sets will still be \visible", though less frequent than originally. For instance, after uniform randomization with p = 80%, an item set of 3 items that originally occurred in 1% transactions will occur in about 1% _ (0:2)3 = 0:008% transactions, which is about 80 transactions per each million. The opposite effect of \false" item sets becoming more frequent is comparatively inconsequential if there are many possible items: for 10,000 items, the probability that, say, 10 randomly inserted items contain a given 3-itemset is less than 10 7%. Unfortunately, this randomization has a problem. If we know that our 3-itemset escapes randomization in 80 per million transactions, and that it is unlikely to occur even once because of randomization, then every time we see it in a randomized transaction we know with near certainty of its presence in the nonrandomized transaction. With even more certainty we will know that at least one item from this item set is \true": as we have mentioned, a chance insertion of only one or two of the items is much more likely than of all three. In this case we can say that a privacy breach has occurred. while privacy is preserved on average, personal information leak through uniform randomization for some fraction of transactions, despite the high value of p.
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Privacy-Preserving Record Linkage and Privacy-Preserving Blocking for Large Files with Cryptographic Keys using Multibit Trees

Privacy-Preserving Record Linkage and Privacy-Preserving Blocking for Large Files with Cryptographic Keys using Multibit Trees

Due to the increasing availability of administrative information, linking different databases to determine the overlap of the databases or to enhance the information available for a cer- tain unit is a widely used strategy for statistical purposes. For example, of the 40 European censuses in 2010 only 21 were traditional censuses, while the rest was based on the linkage of registries (Valente 2010). Linking different databases using a set of common identifiers is trivial if a unique personal identification number (PID) can be used. In some countries (for example, the Scandinavian countries) a PID is available for all members of the pop- ulation. In practice, however, most statistical linkage operations are based on personal identifiers such as the name or date of birth. Such identifiers must be combined to yield an identification code. However, the identifiers are usually neither stable nor recorded with- out errors (Winkler 2009). Therefore, the use of exact matching identifiers will only link a non-randomly selected subset of the records. Hence, methods allowing for small varia- tions of identifiers are to be used. Unfortunately, encryption of identifiers usually restricts linking to exact matching identifiers only. Hence, methods for linking with encrypted iden- tifiers allowing for small errors in identifiers have to be used. Suitable methods are called “privacy preserving record linkage techniques”. A method for privacy preserving record linkage which has recently become popular, is the use of Bloom-Filters.
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SAFETY ISSUES IN DATA MINING

SAFETY ISSUES IN DATA MINING

Privacy-preserving distributed data mining is a multidisciplinary field and requires close cooperation between researchers and practitioners from the fields of cryptography, data mining, public policy and law. Now, the question is how to compute the results without pooling the data in a way that reveals nothing but the final results of the data mining computation [6]. This question of privacy-preserving data mining is actually a special case of a long-studied problem in cryptography called secure multiparty computation. This problem deals with a setting where a set of parties with private inputs wishes to jointly compute some function of their inputs. This joint computation should have the property that the parties learn the correct output and nothing else, even if some of the parties maliciously collude to obtain more information [7]. Clearly, a protocol is needed to solve privacy-preserving distributed data mining problems.
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A Novel Approach for Supporting Privacy Protection in Personalized Web Search by Using Data Mining

A Novel Approach for Supporting Privacy Protection in Personalized Web Search by Using Data Mining

The privacy concern is one of the major barriers in deploying serious personalized search applications, and how to attain personalized search though preserving users’ privacy. Here we propose a client side personalization which deals with the preserving privacy and envision possible future strategies to fully protect user privacy. For privacy, we introduce our approach to digitalized multimedia content based on user profile information. For this, two main methods were developed: Automatic creation of user profiles based on our profile generator mechanism and on the other hand recommendation system based on the content to estimates the user interest based on our client side meta data.
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Privacy-Preserving Statistical Analysis of Health Data Using Paillier Homomorphic Encryption and Permissioned Blockchain

Privacy-Preserving Statistical Analysis of Health Data Using Paillier Homomorphic Encryption and Permissioned Blockchain

In [20], the authors proposed a method for supporting private data in a Hyperledger Fabric channel. The proposed method requires modification of the underlying struc- ture of Fabric’s network for adding two new components. As a showcase, the authors implemented an auction application and stored encrypted reservations and bidding values privately on the ledger. Their results showed a 0.3 s transaction execution time. However, their method requires some clients to have access to the same pri- vate keys that peers use for data encryption, which may raise some security concerns and may not be suitable for the statistical analysis of health records. Compared to our work, we do not require any modification in the underlying Hyperledger Fabric structure and do not distribute the private key between the peers. Our method can be plugged into existing blockchain applications and used instantly. The authors in [48, 21, 46], proposed privacy-preserving techniques and protocols for securely com- puting statistical analysis methods. However, their proposed protocols are highly interactive and require many data exchanges between the participating parties. Our work is an attempt to reduce this complexity by using the blockchain technology.
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Preserving the Privacy and Sharing the Data Using Classification on Perturbed Data

Preserving the Privacy and Sharing the Data Using Classification on Perturbed Data

Data mining extracts useful patterns from large quantities of data stored in the data warehouse. The data mining process results valuable patterns to support decision making in different domains. But easy access to sensitive data poses threat to individual privacy. In this paper we presented a novel approach in which both data perturbation technique and classification are integrated to provide better data quality and individual privacy both at data owner site as well as at data mining site. The owner’s data consists of both categorical and numeric data types. To preserve the privacy of data at owner’s site perturbation technique is used in which small amount of noise is added to sensitive data such that the properties and the meaning of the original data is not changed. The problem with the randomization technique is that some privacy intrusion techniques can be used to reconstruct private information from the randomized data tuples. Hence to enhance the performance a decision tree is built on the perturbed data at data mining site, which reveals and gives only the required results and hides other information.
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PERSONALIZED PRIVACY PRESERVING USING SENSITIVE ATTRIBUTE GENERALIZATION

PERSONALIZED PRIVACY PRESERVING USING SENSITIVE ATTRIBUTE GENERALIZATION

As k-anonymity has several drawbacks, the concept of personalized anonymity is used. The proposed system is a new generalization framework based on the concept of personalized anonymity, as k-anonymity has several drawbacks. A simple taxonomy on attribute Disease is accessible by the public It organizes all diseases as leaves of a tree as shown in Figure 3. An intermediate node carries a name summarizing the diseases in its sub tree. Individual may specify node as the “guarding node” for his privacy, for sensitive attribute value. An individual may specify which implicit node of the taxonomy underneath all the leave is used. The empty- set preference implies that he is willing to release his actual diagnosis result for e.g. flu for Lisa in Figure 1; therefore it can be published directly.
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Publishing data from electronic health records while preserving privacy: a survey of algorithms

Publishing data from electronic health records while preserving privacy: a survey of algorithms

and Female are the leaf-level nodes of a taxonomy for Gender, whose root value and immediate ascendant of the leaves is Any. Thus, we can combine these two taxonomies to get a lattice for Ethnicity and Gender. Each node in this lat- tice represents a different set of generalized values for Ethnicity and Gender, such as {English, Male}, {English, Female}, {Welsh, Male}, and {British, Any}. Thus, finding a way to generalize values can be performed by exploring the lattice using heuristics that avoid considering certain lattice nodes for efficiency reasons. The strategy (i) prunes the ascendants of lattice nodes that are sufficient to satisfy a privacy model, while the strategies (ii) and (iii) prune lattice nodes that are likely to incur high utility loss. The latter nodes are identified while considering nodes that represent incrementally larger sets of generalized values, for strategy (i), or while selecting nodes by combining their descendants, as specified by a genetic algorithm, in the case of strategy (ii).
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Title: The Combined Technique Of Various Algorithms For Privacy Preserving In Data Mining To Provide Privacy For Sensitive Data In Data Sets

Title: The Combined Technique Of Various Algorithms For Privacy Preserving In Data Mining To Provide Privacy For Sensitive Data In Data Sets

Abstract- Organizations consists of a large amount of data which is been collected for the data mining tasks. The data will consist of two types of information such as sensitive information and non-sensitive information. The data when released to the miner the privacy preserving is an important issue. In order to overcome this issue the efficient combined technique of various data mining algorithms have been proposed for providing privacy for sensitive data in different types of data sets. The combined techniques of the algorithms called randomization and k-anonymization (R-Ka) which aims to protect the sensitive data with less information loss and hence maximize the data usage which will prevent from various types of attacks and also helps to reconstructs the data. The second combined technique called as blocking based cryptographic technique (BBCT) which will replace the known values with the unknown values and encrypt and suppress the sensitive attributes. The third combined technique called as randomization cryptographic technique (RCT) which helps in building the random decision tree (RDT) which provides improved efficiency and security for various decision tree based tasks.
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ACM Access control using k-anonymity, query evaluate on outsourced database

ACM Access control using k-anonymity, query evaluate on outsourced database

Micro data refers to series of records, each record with information on an individual unit like a patient or an organization. Access Control Mechanisms (ACM) safe the sensitive information from unauthorized users. Even sanctioned users may misuse the data to reveal the privacy of individuals to whom the data refers to. Privacy prosperous Mechanism (PPM) anonymize the relational data to avoid identity and attribute disclosure. It is achieved by generalization or suppression. Role- predicated access control gives users the sanctions to access the data predicated on their roles. The Access Control Mechanism define sallow predicates available to purposes while the privacy required is to fill the k-anonymity or l-diversity. Define bound constraint is assigned for each individual predicate. Top down Selection Mondrian (TDSM) algorithm is utilized for query workload-predicated anonymization algorithm is constructed utilizing acquisitive heuristics and kd-tree model. Query cuts are culled with minimum bounds in Top-Down Heuristic1 algorithm (TDH1). The query jumps are modified as the partitions are integrated to the output in Top-Down Heuristic 2 algorithm (TDH2). The price of decreasedaccurecy in the query results is utilized in Top-Down Heuristic 3 algorithm (TDH3). Repartitioning algorithm is utilized to reduce the total imprecision for the queries. The privacy preserved access control framework is enhanced to provide incremental mining features utilizing R+-tree. Data insert, expunge and update operations are associated with the partition management mechanism.
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Privacy Preserving Unstructured Data Publishing (PPUDP) Approach for Big Data

Privacy Preserving Unstructured Data Publishing (PPUDP) Approach for Big Data

The majority of Big Data is of type Unstructured namely audio, video, text etc. The unstructured data like text are difficult to analyze and contains knowledge that can be extracted by text mining techniques resulting in privacy threat. To avoid the threats text documents has to be transformed into a form to preserve privacy before publishing to Big Data Analytics process. The techniques used to preserve data privacy prior to data analysis called privacy preserving data publishing(PPDP).In order to preserve the privacy of the unstructured text data the first step is to apply pre processing techniques on text data and second is to categorize text documents into secured or not secured based on content. It requires domain knowledge to complete extent to determine secure features from text data.[1].The “unstructured” intends free form distribution of potentially sensitive data in text files with controls possibly become ineffective outside of specific systems or applications. Once the text file is shared, there is a possibility of high security
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A new differential privacy preserving crowdsensing scheme based on the Owen value

A new differential privacy preserving crowdsensing scheme based on the Owen value

The Internet of Everything (IoE) paradigm makes the Internet more pervasive, interconnecting every devices of everyday life, and it is a promising solution for the development of 5G network services. Nowadays, Internet- connected devices are equipped with various built-in sensors. Therefore, the concept of mobile crowdsensing (MC) has been introduced to the IoE-driven situation where mobile devices gather data with the aim of performing a specific application. In this paper, we propose a new cooperative game model for the privacy-driven device collaboration in the MC system. The major goal of our approach is to incentivize the participating devices for effective data acquisitions while protecting each individual privacy based on each device ’ s preference. According to the Owen value mechanism, the proposed scheme provides an effective payment solution for each MC participating device under privacy considered IoE environments. The main merit possessed by our MC control approach is to guide the cooperation of mobile devices in providing MC services. Performance evaluation reveals the superiority of our proposed scheme in terms of task success ratio, MC participating ratio, and payoff fairness. Finally, we provide the guidance on the future research direction of the MC system including other issues.
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A privacy preserving exception handling approach for dynamic mobile crowdsourcing applications

A privacy preserving exception handling approach for dynamic mobile crowdsourcing applications

The ever-increasing popularity of mobile devices has created a variety of crowdsourcing applications by employing the massive and distributed mobile comput- ing resources. However, in the mobile crowdsourcing process, a previously selected worker may become un- available due to various exceptions. In this situation, it is significant for the crowdsourcing platform to quickly find another similar worker to replace the unavailable worker, in an efficient and privacy-preserving way. In view of this challenge, a novel privacy-preserving excep- tion handling approach, named ExH Simhash , is put for-
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