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Journal articles on the topic 'Probabilistic data encryption'

Author: Grafiati
Published: 30 May 2022
Last updated: 31 May 2022

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1

Mazumder, Rashed, Atsuko Miyaji, and Chunhua Su. "Probably Secure Keyed-Function Based Authenticated Encryption Schemes for Big Data." International Journal of Foundations of Computer Science 28, no. 06 (2017): 661–82. http://dx.doi.org/10.1142/s0129054117400123.

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Security, privacy and data integrity are the critical issues in Big Data application of IoT-enable environment and cloud-based services. There are many upcoming challenges to establish secure computations for Big Data applications. Authenticated encryption (AE) plays one of the core roles for Big Data’s confidentiality, integrity, and real-time security. However, many proposals exist in the research area of authenticated encryption. Generally, there are two concepts of nonce respect and nonce reuse under the security notion of the AE. However, recent studies show that nonce reuse needs to sacrifice security bound of the AE. In this paper, we consider nonce respect scheme and probabilistic encryption scheme which are more efficient and suitable for big data applications. Both schemes are based on keyed function. Our first scheme (FS) operates in parallel mode whose security is based on nonce respect and supports associated data. Furthermore, it needs less call of functions/block-cipher. On the contrary, our second scheme is based on probabilistic encryption. It is expected to be a light solution because of weaker security model construction. Moreover, both schemes satisfy reasonable privacy security bound.
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Leung, K. C., S. L. Li, L. M. Cheng, and C. K. Chan. "A Symmetric Probabilistic Encryption Scheme Based On CHNN Without Data Expansion." Neural Processing Letters 24, no. 2 (2006): 93–105. http://dx.doi.org/10.1007/s11063-006-9006-9.

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3

Aruna, M. G., and K. G. Mohan. "Secured cloud data migration technique by competent probabilistic public key encryption." China Communications 17, no. 5 (2020): 168–90. http://dx.doi.org/10.23919/jcc.2020.05.014.

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Kulshrestha, Vartika, Seema Verma, and C. Rama Krishna. "Hybrid probabilistic triple encryption approach for data security in cloud computing." International Journal of Advanced Intelligence Paradigms 21, no. 1/2 (2022): 158. http://dx.doi.org/10.1504/ijaip.2022.121035.

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Zhu, Li Juan. "A Network Identity Authentication Protocol Based on Fingerprint and Probabilistic Encryption of RSA." Applied Mechanics and Materials 241-244 (December 2012): 2471–74. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.2471.

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Security transaction problem between the client and the server was solved by a network identity authentication protocol of this paper. We designed this protocol from two aspects, one is the hardware structure design, and the other is data encryption. We used fingerprint sensor to gather the user’s fingerprint and characteristic value instead of password input from keyboard, and used RSA probabilistic encryption to generate public key and private key of the client and public key and private key of the server, and then used the protocol certification process to finish operation of the data encryption and decryption. This protocol guarantees every specific legitimate user rights and prevents the hacker from attacking network data, and the practical application shows this protocol is absolutely safe.
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Kulshrestha, Vartika, Seema Verma, and C. Rama Krishna. "Hybrid Probabilistic Triple Encryption (HPRRA) Approach for Data Security in Cloud Computing." International Journal of Advanced Intelligence Paradigms 1, no. 1 (2021): 1. http://dx.doi.org/10.1504/ijaip.2021.10036005.

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Luna, Jose Marcio, Chaouki T. Abdallah, and Gregory L. Heileman. "Probabilistic Optimization of Resource Distribution and Encryption for Data Storage in the Cloud." IEEE Transactions on Cloud Computing 6, no. 2 (2018): 428–39. http://dx.doi.org/10.1109/tcc.2016.2543728.

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Zhang, Jing-Wen, Xiu-Bo Chen, Gang Xu, and Yi-Xian Yang. "Universal quantum circuit evaluation on encrypted data using probabilistic quantum homomorphic encryption scheme*." Chinese Physics B 30, no. 7 (2021): 070309. http://dx.doi.org/10.1088/1674-1056/ac003b.

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9

Gandhi, Mehak, and Kiranbir Kaur. "Enhancing Secure and Efficient Online Data Storage over Cloud Using Homomorphic with Probabilistic Encryption." International Journal of Computer Sciences and Engineering 6, no. 3 (2018): 263–68. http://dx.doi.org/10.26438/ijcse/v6i3.263268.

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Et.al, Quazi Warisha Ahmed. "Energetic Data Security Management Scheme using Hybrid Encryption Algorithm over Cloud Environment." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (2021): 201–8. http://dx.doi.org/10.17762/turcomat.v12i6.1289.

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Now-a-days all documents are in a digital format as well as everyone need to maintain their data in electronic mode with the help of cloud servers. A cloud server provides lots of facilities to users such as remote data maintenance, huge data handling and so on. But in the case of security many cloud servers are providing probabilistic results alone. So, that a new cloud server data maintenance scheme is required to provide a high-level data security to the cloud system in an efficient manner. This paper introduces a new crypto-approach called Novel Hybrid Encryption Mode (NHEM), in which this algorithm integrates several latest approaches of crypto logics into it and provides a top-end security level to the cloud data. This NHEM is derived from the base factors of two cryptographic algorithms called Advanced Encryption Standard with 512-bit size of key frequency with Message Digest (MD5) algorithm. By using the integration of these two powerful approaches, a novel approach called NHEM is designed and it provides a huge support to preserve security on cloud medium. The cloud data needs to be protected from unwanted threats and intruders by means of raising an interruption attacks on server. The proposed approach of Novel Hybrid Encryption Mode concentrates more on access control logics and the crypto norms with respect to privacy measures. In this paper, the proposed scheme assures the data is too robust and no one can attack the data without proper credentials as well as the outcome proofs are clearly given on the resulting unit of this paper. The Advanced Encryption Standard algorithm is a well-known and powerful crypto scheme and the MD5 algorithm is also considered to be the unique unidirectional algorithm to provide security level in energetic manner. These two algorithms are combined together with the bit frequency of 512 to achieve the highest accuracy levels in data security over cloud environment as well as the resulting portion clearly illustrates that with proper graphical outcome.
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Jayapandian, N., and A. M. J. Md Zubair Rahman. "Secure and efficient online data storage and sharing over cloud environment using probabilistic with homomorphic encryption." Cluster Computing 20, no. 2 (2017): 1561–73. http://dx.doi.org/10.1007/s10586-017-0809-4.

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Wei, Hong Ru, and Ya Fei Zheng. "Algebraic Techniques in Linear Cryptanalysis." Advanced Materials Research 756-759 (September 2013): 3634–39. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3634.

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Linear cryptanalysis is a statistical analysis method. Linear cryptanalysis constructs probabilistic patterns first and then distinguishes the cipher from a random permutation using lots of plaintext-ciphertext pairs. Linear cryptanalysis has a big data complexity. Algebraic attack attempts to exploit the algebraic structure of the cipher by expressing the encryption transformation as a set of polynomial equations and then attempts to solve the system to recover the encryption key. Algebraic attacks do not need too much data. This paper combines these two methods by using algebraic techniques in linear cryptanalysis, and proposes a new cryptanalysis method called Algebraic Techniques in Linear Cryptanalysis. This new method is used in the existing linear cryptanalysis of PRESENT. To recover 8-bit key information of 21-round PRESENT, the data complexity is2'and the time complexity is 2'.Compared with the result of linear cryptanalysis, the data complexity is obviously decreased.
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13

Senthil Kumar, K., and D. Malathi. "A Novel method to represent Access Tree structure by Context Free Grammar with and-or graph in Key Policy based Attribute based Encryption." International Journal of Engineering & Technology 7, no. 4.10 (2018): 396. http://dx.doi.org/10.14419/ijet.v7i4.10.20946.

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Important and sensitivity data of users in a third party managed internet or cloud always pose various security as well as privacy issues. Attribute-based encryption (ABE) is a pleasant trend in the literature which addresses above problem in an efficient way, and provides data security and fine-grained access control in a decentralized manner,. Key-policy attribute-based encryption (KP-ABE) is an important type of ABE, in which user can decrypt his message with a set of attributes and private keys are embedded with a access control structure which defines which cipher text an user can be allowed to decrypt. In this paper we use a probabilistic context free grammar with an And-Or structure to represent access control structure. And-Or graph has high expressive power hence access control structure can be represented in an efficient manner.
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Prakash, A. John, and B. Lydia Elizabeth. "Pindex: Private multi-linked index for encrypted document retrieval." PLOS ONE 16, no. 8 (2021): e0256223. http://dx.doi.org/10.1371/journal.pone.0256223.

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Cryptographic cloud storage is used to make optimal use of the cloud storage infrastructure to outsource sensitive and mission-critical data. The continuous growth of encrypted data outsourced to cloud storage requires continuous updating. Attacks like file-injection are reported to compromise confidentiality of the user as a consequence of information leakage during update. It is required that dynamic schemes provide forward privacy guarantees. Updates should not leak information to the untrusted server regarding the previously issued queries. Therefore, the challenge is to design an efficient searchable encryption scheme with dynamic updates and forward privacy guarantees. In this paper, a novel private multi-linked dynamic index for encrypted document retrieval namely Pindex is proposed. The multi-linked dynamic index is constructed using probabilistic homomorphic encryption mechanism and secret orthogonal vectors. Full security proofs for correctness and forward privacy in the random oracle model is provided. Experiments on real world Enron dataset demonstrates that our construction is practical and efficient. The security and performance analysis of Pindex shows that the dynamic multi-linked index guarantees forward privacy without significant loss of efficiency.
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15

Dadheech, Pankaj, Abolfazl Mehbodniya, Shivam Tiwari, et al. "Zika Virus Prediction Using AI-Driven Technology and Hybrid Optimization Algorithm in Healthcare." Journal of Healthcare Engineering 2022 (January 12, 2022): 1–13. http://dx.doi.org/10.1155/2022/2793850.

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The Zika virus presents an extraordinary public health hazard after spreading from Brazil to the Americas. In the absence of credible forecasts of the outbreak's geographic scope and infection frequency, international public health agencies were unable to plan and allocate surveillance resources efficiently. An RNA test will be done on the subjects if they are found to be infected with Zika virus. By training the specified characteristics, the suggested Hybrid Optimization Algorithm such as multilayer perceptron with probabilistic optimization strategy gives forth a greater accuracy rate. The MATLAB program incorporates numerous machine learning algorithms and artificial intelligence methodologies. It reduces forecast time while retaining excellent accuracy. The projected classes are encrypted and sent to patients. The Advanced Encryption Standard (AES) and TRIPLE Data Encryption Standard (TEDS) are combined to make this possible (DES). The experimental outcomes improve the accuracy of patient results communication. Cryptosystem processing acquires minimal timing of 0.15 s with 91.25 percent accuracy.
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16

Yu, Yun William, and Griffin M. Weber. "Balancing Accuracy and Privacy in Federated Queries of Clinical Data Repositories: Algorithm Development and Validation." Journal of Medical Internet Research 22, no. 11 (2020): e18735. http://dx.doi.org/10.2196/18735.

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Background Over the past decade, the emergence of several large federated clinical data networks has enabled researchers to access data on millions of patients at dozens of health care organizations. Typically, queries are broadcast to each of the sites in the network, which then return aggregate counts of the number of matching patients. However, because patients can receive care from multiple sites in the network, simply adding the numbers frequently double counts patients. Various methods such as the use of trusted third parties or secure multiparty computation have been proposed to link patient records across sites. However, they either have large trade-offs in accuracy and privacy or are not scalable to large networks. Objective This study aims to enable accurate estimates of the number of patients matching a federated query while providing strong guarantees on the amount of protected medical information revealed. Methods We introduce a novel probabilistic approach to running federated network queries. It combines an algorithm called HyperLogLog with obfuscation in the form of hashing, masking, and homomorphic encryption. It is tunable, in that it allows networks to balance accuracy versus privacy, and it is computationally efficient even for large networks. We built a user-friendly free open-source benchmarking platform to simulate federated queries in large hospital networks. Using this platform, we compare the accuracy, k-anonymity privacy risk (with k=10), and computational runtime of our algorithm with several existing techniques. Results In simulated queries matching 1 to 100 million patients in a 100-hospital network, our method was significantly more accurate than adding aggregate counts while maintaining k-anonymity. On average, it required a total of 12 kilobytes of data to be sent to the network hub and added only 5 milliseconds to the overall federated query runtime. This was orders of magnitude better than other approaches, which guaranteed the exact answer. Conclusions Using our method, it is possible to run highly accurate federated queries of clinical data repositories that both protect patient privacy and scale to large networks.
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17

Kjamilji, Artrim. "Techniques and Challenges while Applying Machine Learning Algorithms in Privacy Preserving Fashion." Proceeding International Conference on Science and Engineering 3 (April 30, 2020): xix. http://dx.doi.org/10.14421/icse.v3.600.

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Nowadays many different entities collect data of the same nature, but in slightly different environments. In this sense different hospitals collect data about their patients’ symptoms and corresponding disease diagnoses, different banks collect transactions of their customers’ bank accounts, multiple cyber-security companies collect data about log files and corresponding attacks, etc. It is shown that if those different entities would merge their privately collected data in a single dataset and use it to train a machine learning (ML) model, they often end up with a trained model that outperforms the human experts of the corresponding fields in terms of accurate predictions. However, there is a drawback. Due to privacy concerns, empowered by laws and ethical reasons, no entity is willing to share with others their privately collected data. The same problem appears during the classification case over an already trained ML model. On one hand, a user that has an unclassified query (record), doesn’t want to share with the server that owns the trained model neither the content of the query (which might contain private data such as credit card number, IP address, etc.), nor the final prediction (classification) of the query. On the other hand, the owner of the trained model doesn’t want to leak any parameter of the trained model to the user. In order to overcome those shortcomings, several cryptographic and probabilistic techniques have been proposed during the last few years to enable both privacy preserving training and privacy preserving classification schemes. Some of them include anonymization and k-anonymity, differential privacy, secure multiparty computation (MPC), federated learning, Private Information Retrieval (PIR), Oblivious Transfer (OT), garbled circuits and/or homomorphic encryption, to name a few. Theoretical analyses and experimental results show that the current privacy preserving schemes are suitable for real-case deployment, while the accuracy of most of them differ little or not at all with the schemes that work in non-privacy preserving fashion.
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"Identity Based Data Sharing and Profile Matching using Probabilistic Key in Cloud." International Journal of Recent Technology and Engineering 8, no. 6 (2020): 3076–80. http://dx.doi.org/10.35940/ijrte.f8434.038620.

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Cloud technology is popular since the utilizations and their information are expanding hugely step by step. It gives usefulness to overseeing data information in a conveyed and pervasive way supporting many platform. Data sharing and security problem are the principle issue to the increasing use of health care, since health information is delicate. However issues, for example, risks of privacy exposure ,versatility in key administration, adaptable access and effective uses denial always been the most significant difficulties for accomplishing goodgrained cryptographically upheld information access control. Right now structure a protected information move and profile coordinating plan with probabilistic open key encryption in correspondence test to diminish the size of open key which helps in decreasing the capacity and furthermore time productive. Our probabilistic open key encryption plot when executed in a bilinear gathering, it can check whether two figure writings are scrambled of a similar message. Strangely, in encryption or decoding system bilinear mapping isn't required in Public Key Encryption conspire except if when individuals need to do in fairness test between two figure messages that might be produced utilizing diverse open keys. Additionally the profile coordinating component dependent on Probabilistic Public Key Encryption with fairness test( PPKEET) causes patient to discover companions in a security saving way with age of trapdoors and structure social connections as indicated by the desire. The security examination and trial results demonstrated that our plan is ensuring the information and giving right medicine in cloud.
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"A Symmetric Searchable Encryption Identification of Data on Probabilistic Trapdoors." International Journal of Engineering and Advanced Technology 9, no. 3 (2020): 3880–83. http://dx.doi.org/10.35940/ijeat.c6389.029320.

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Accessible Encryption (SE) permits a client in accordance with transfer records in accordance with the astronaut and according to seem thru it of a faraway manner while defending the security concerning each the statistics yet the inquiries. Right now, entrust a generative then the simple in imitation of actualize Symmetric Searchable Encryption Scheme because instance (SSE) [4]. This tale takes the some round concerning correspondence namely O(n) instances on calculations upstairs n range regarding records . We likewise presented some other variety of Search Pattern Privacy, as gives a percentage about safety on the spillage structure trapdoor [4]. We also advocate the modifications over our graph because of batch inquire who can not reap the versatile vagary recipe. The current method offers the appropriateness whilst maintaining stuff about the data classified, that is viable so much such has advise bother among the full altar about the demand bill and wants in accordance with remove watchwords now the archives are eke out away. We likewise recommend adjustments in our format so the graph execute be utilized upon the potent enemies at the fee on various rounds of transmission yet the inclination space. [6]We may eke exhibit our layout on a range of commercial enterprise datasets. Likewise correct now, utilizes the Hash tying strategy instead than band over encryption action because file for consideration majority which makes such splendid for light poise applications. We are the first in imitation of suggest pardon trapdoors between Symmetric Searchable Encryption because the tussock search.
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Nagnath, Ingale Ashwini. "A Modified Approach on Security Analysis Using Probabilistic Order Preserving Encryption Based on Cloud Data Serach." International Journal Of Engineering And Computer Science, January 30, 2017. http://dx.doi.org/10.18535/ijecs/v6i1.58.

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"Enhanced Security of Encrypted Text by KDMT: Key-Domain Maximization Technique." International Journal of Recent Technology and Engineering 8, no. 5 (2020): 1385–88. http://dx.doi.org/10.35940/ijrte.e6326.018520.

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Encryption-decryption techniques have been the backbone of network security in the modern era of wireless transmission of data. We present here a more secured encryption-decryption method based on the maximization of key domain in finite field. The proposed technique uses a random primary key to fetch the encryption-decryption key-pair furnished by a unique decomposition. A secondary key taken from a subdomain with specific property is used to add more randomness in the encrypted text structure. A probabilistic comparison of key prediction by hacker is also discussed to justify the added security in the proposed method.
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Jalasri, M., and L. Lakshmanan. "Managing data security in fog computing in IoT devices using noise framework encryption with power probabilistic clustering algorithm." Cluster Computing, May 17, 2022. http://dx.doi.org/10.1007/s10586-022-03606-2.

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23

Thurairasu, Lisa, and Nelson Chong. "Information Management at a Health Services Research Organization in Toronto, Ontario, Canada: Moving from Identifiable Data to Coded Data." International Journal of Population Data Science 1, no. 1 (2017). http://dx.doi.org/10.23889/ijpds.v1i1.307.

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ABSTRACTObjectivesA health services research organization in Toronto, Ontario, Canada conducts population-based research to improve the health of Canadians in seven main areas: (1) cancer, (2) cardiovascular disease, (3) chronic disease and pharmacology, (4) health system planning and evaluation, (5) kidney, dialysis and transplantation, (6) mental health and addictions, and (7) primary care and population health. The Information Management (IM) team within the Data Quality and Information Management (DQIM) department at our non-profit organization is an integral component for upholding privacy and confidentiality policies and procedures while facilitating quality research using different types of data such as health administrative, third-party, primary data collection, and electronic medical records (EMR).
 MethodsThe IM team is responsible for receiving data, encoding direct personal identifiers, screening for unnecessary identifiers, performing probabilistic data linkage when necessary, importing the data to the Research Analytics Environment (a client/server Linux-based system), and destroying the data according to the terms stipulated in the executed data sharing agreement. The purpose of the presentation is to detail the above steps of processing data to protect individuals’ identities yet preserve the usefulness of carrying out research. The presentation will include aspects from importing data into SAS to storage and encoding of personal identifiers to probabilistic data linkage, which involves maximizing linkage with other datasets at the organization. Linking data at the organization involves the encryption or encoding of health card numbers to “Key Numbers.”
 ResultsThe processing practices used at the organization comply with Canadian privacy laws such as the Personal Health Information Protection Act (PHIPA) as well as organizational policies and Research Ethics Board approvals. The approaches used to conceal individual identities yet allow linkage to various data sources can be modelled by other health agencies, ministries, and non-health related organizations that work with sensitive data but face challenges in maintaining both privacy and research quality. Our organization strives to make processing as efficient as possible and create maximum linkability to the various data sources in house while upholding privacy and confidentiality.
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Anwarbasha, H., S. Sasi Kumar, and D. Dhanasekaran. "An efficient and secure protocol for checking remote data integrity in multi-cloud environment." Scientific Reports 11, no. 1 (2021). http://dx.doi.org/10.1038/s41598-021-93073-3.

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AbstractIn cloud environment, huge quantity of data has been generated at each and every second. In order to manage the data, cloud service provider makes use of multi-cloud platform to fulfill the requirement. The service provider cooperatively operates altogether for the accessibility of resources and are improvised by implementing the dynamic operation that can run at a time through the Multi-cloud environment. This paper presents a Dynamic Level Based Integrity Checking Protocol (DA-ICP) for storing data in multicloud environment. The proposed method introduces Provable Data Possession (PDP) approach which enables a user who outsources the data at an untrusted multi-cloud for ensuring that the server possesses the original data without downloading it. This model creates a probabilistic proof of possession by sampling an arbitrary collection of blocks from server that considerably minimizes the cost. The effective and secured outsourced data has been resolved using public key cryptography and undergo encryption using Efficient-PDP (EPDP). During experimentation, the presented DA-ICP shows a maximum accuracy of 96.78%. The proposed method uses Multi-cloud in DA-ICP which produces an efficient output than other existing techniques.
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Derler, David, Kai Gellert, Tibor Jager, Daniel Slamanig, and Christoph Striecks. "Bloom Filter Encryption and Applications to Efficient Forward-Secret 0-RTT Key Exchange." Journal of Cryptology 34, no. 2 (2021). http://dx.doi.org/10.1007/s00145-021-09374-3.

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AbstractForward secrecy is considered an essential design goal of modern key establishment (KE) protocols, such as TLS 1.3, for example. Furthermore, efficiency considerations such as zero round-trip time (0-RTT), where a client is able to send cryptographically protected payload data along with the very first KE message, are motivated by the practical demand for secure low-latency communication. For a long time, it was unclear whether protocols that simultaneously achieve 0-RTT and full forward secrecy exist. Only recently, the first forward-secret 0-RTT protocol was described by Günther et al. (Eurocrypt, 2017). It is based on puncturable encryption. Forward secrecy is achieved by “puncturing” the secret key after each decryption operation, such that a given ciphertext can only be decrypted once (cf. also Green and Miers, S&P 2015). Unfortunately, their scheme is completely impractical, since one puncturing operation takes between 30 s and several minutes for reasonable security and deployment parameters, such that this solution is only a first feasibility result, but not efficient enough to be deployed in practice. In this paper, we introduce a new primitive that we term Bloom filter encryption (BFE), which is derived from the probabilistic Bloom filter data structure. We describe different constructions of BFE schemes and show how these yield new puncturable encryption mechanisms with extremely efficient puncturing. Most importantly, a puncturing operation only involves a small number of very efficient computations, plus the deletion of certain parts of the secret key, which outperforms previous constructions by orders of magnitude. This gives rise to the first forward-secret 0-RTT protocols that are efficient enough to be deployed in practice. We believe that BFE will find applications beyond forward-secret 0-RTT protocols.
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Liu, Yiming, Qi Zeng, Yue Zhao, Kaijun Wu, and Yao Hao. "Novel channel-hopping pattern-based wireless IoT networks in smart cities for reducing multi-access interference and jamming attacks." EURASIP Journal on Wireless Communications and Networking 2021, no. 1 (2021). http://dx.doi.org/10.1186/s13638-021-02029-8.

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AbstractIn smart cities, the Internet-of-Thing (IoT) provides an enabling communication infrastructure to support tremendous amount of data exchange. Most IoT applications, e.g., wireless local area network, Bluetooth and so forth, utilize the channel-hopping scheme to suppress the transmission security threats. In this paper, to reduce the mutual interference and external jamming attacks, multiple novel channel-hopping patterns, i.e., traditional no-hit-zone (NHZ) hopping pattern and generalized NHZ hopping pattern, are introduced to suit to IoT networks. Particularly the design of probabilistic hopping pattern is first proposed, which has the various usage probabilities with regard to various channels. The properties of these hopping patterns are investigated by the step-to-step examples. Then, the error-rate performance of the multi-node IoT systems adopting these hopping patterns in the presence of jamming attacks is comprehensively analyzed. The extensive simulations show that the traditional/generalized NHZ hopping patterns are in favor of combating the mutual interference but with the limited capability of reducing jamming attacks, while the probabilistic hopping pattern possesses the opposite feature, that is, it has the predominant merit in suppressing jamming attacks. Thus, the novel channel-hopping pattern-based IoT could provide the secure transmission for communication applications in smart cities. Note that, as the physical-layer security technique, the channel-hopping patterns investigated in this paper are convenient to integrate with the security policies implemented in upper layers (e.g., encryption, authentication and so forth).
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Schnell, Rainer, and Christian Borgs. "Proof of Concept for a Privacy Preserving National Mortality Register." International Journal of Population Data Science 3, no. 4 (2018). http://dx.doi.org/10.23889/ijpds.v3i4.732.

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IntroductionNational mortality registers are essential for medical research. Therefore, most nations operate such registers. Due to the administrative structure and data protection legislation, there is no such registry in Germany. We demonstrate that a national mortality registry is technically feasible under the given constraints with privacy preserving record linkage (PPRL).
 Objectives and ApproachGetting the legal permission to operate a national mortality registry for research will be easier if the linkage can be done without revealing personal identifiers by using PPRL. To estimate precision and recall of different encodings, we used two settings: (1) matching a local mortality registry (n = 14,003) with mortality data of a university hospital (n = 2,466); (2) matching 1 million simulated records from a national database of names with a corrupted subset. This corresponds to a match of all deceased persons with the deceased persons in the largest federal state (n = 205,000).
 ResultsLinkage results for clear-text identifiers show very high recall and precision. Bloom-Filter based encryptions yield comparable results. Neither precision nor recall declines more than 2%. Phonetic codes yield high precision but low recall. Some variants of Bloom Filter-based encodings yield better results than probabilistic linkage on clear-text identifiers. This is mainly due to the rarely mentioned detail of using different passwords for different identifiers in the same Bloom Filter. Therefore, implementation details of Bloom Filters are more important than commonly thought. Overall, we recommend the use of salted Bloom Filter-based methods with different passwords for different identifiers to increase security and to prevent all known attacks on identifier encryptions.
 Conclusion/ImplicationsAlthough most PPRL techniques would yield acceptable results in the given setting of a national register, salted Bloom filter encodings are more secure against attacks while still showing high precision and recall. Therefore, we consider a national mortality register using only encrypted identifiers of deceased persons as feasible.
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