Visible to the public Biblio

Filters: Keyword is privacy preservation  [Clear All Filters]
2021-05-25
Tian, Nianfeng, Guo, Qinglai, Sun, Hongbin, Huang, Jianye.  2020.  A Synchronous Iterative Method of Power Flow in Inter-Connected Power Grids Considering Privacy Preservation: A CPS Perspective. 2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2). :782–787.
The increasing development of smart grid facilitates that modern power grids inter-connect with each other and form a large power system, making it possible and advantageous to conduct coordinated power flow among several grids. The communication burden and privacy issue are the prominent challenges in the application of synchronous iteration power flow method. In this paper, a synchronous iterative method of power flow in inter-connected power grid considering privacy preservation is proposed. By establishing the masked model of power flow for each sub-grid, the synchronous iteration is conducted by gathering the masked model of sub-grids in the coordination center and solving the masked correction equation in a concentration manner at each step. Generally, the proposed method can concentrate the major calculation of power flow on the coordination center, reduce the communication burden and guarantee the privacy preservation of sub-grids. A case study on IEEE 118-bus test system demonstrate the feasibility and effectiveness of the proposed methodology.
2021-03-29
Kotra, A., Eldosouky, A., Sengupta, S..  2020.  Every Anonymization Begins with k: A Game-Theoretic Approach for Optimized k Selection in k-Anonymization. 2020 International Conference on Advances in Computing and Communication Engineering (ICACCE). :1–6.
Privacy preservation is one of the greatest concerns when data is shared between different organizations. On the one hand, releasing data for research purposes is inevitable. On the other hand, sharing this data can jeopardize users' privacy. An effective solution, for the sharing organizations, is to use anonymization techniques to hide the users' sensitive information. One of the most popular anonymization techniques is k-Anonymization in which any data record is indistinguishable from at least k-1 other records. However, one of the fundamental challenges in choosing the value of k is the trade-off between achieving a higher privacy and the information loss associated with the anonymization. In this paper, the problem of choosing the optimal anonymization level for k-anonymization, under possible attacks, is studied when multiple organizations share their data to a common platform. In particular, two common types of attacks are considered that can target the k-anonymization technique. To this end, a novel game-theoretic framework is proposed to model the interactions between the sharing organizations and the attacker. The problem is formulated as a static game and its different Nash equilibria solutions are analytically derived. Simulation results show that the proposed framework can significantly improve the utility of the sharing organizations through optimizing the choice of k value.
2021-02-22
Martinelli, F., Marulli, F., Mercaldo, F., Marrone, S., Santone, A..  2020.  Enhanced Privacy and Data Protection using Natural Language Processing and Artificial Intelligence. 2020 International Joint Conference on Neural Networks (IJCNN). :1–8.

Artificial Intelligence systems have enabled significant benefits for users and society, but whilst the data for their feeding are always increasing, a side to privacy and security leaks is offered. The severe vulnerabilities to the right to privacy obliged governments to enact specific regulations to ensure privacy preservation in any kind of transaction involving sensitive information. In the case of digital and/or physical documents comprising sensitive information, the right to privacy can be preserved by data obfuscation procedures. The capability of recognizing sensitive information for obfuscation is typically entrusted to the experience of human experts, who are over-whelmed by the ever increasing amount of documents to process. Artificial intelligence could proficiently mitigate the effort of the human officers and speed up processes. Anyway, until enough knowledge won't be available in a machine readable format, automatic and effectively working systems can't be developed. In this work we propose a methodology for transferring and leveraging general knowledge across specific-domain tasks. We built, from scratch, specific-domain knowledge data sets, for training artificial intelligence models supporting human experts in privacy preserving tasks. We exploited a mixture of natural language processing techniques applied to unlabeled domain-specific documents corpora for automatically obtain labeled documents, where sensitive information are recognized and tagged. We performed preliminary tests just over 10.000 documents from the healthcare and justice domains. Human experts supported us during the validation. Results we obtained, estimated in terms of precision, recall and F1-score metrics across these two domains, were promising and encouraged us to further investigations.

2021-02-16
Wu, J. M.-T., Srivastava, G., Pirouz, M., Lin, J. C.-W..  2020.  A GA-based Data Sanitization for Hiding Sensitive Information with Multi-Thresholds Constraint. 2020 International Conference on Pervasive Artificial Intelligence (ICPAI). :29—34.
In this work, we propose a new concept of multiple support thresholds to sanitize the database for specific sensitive itemsets. The proposed method assigns a stricter threshold to the sensitive itemset for data sanitization. Furthermore, a genetic-algorithm (GA)-based model is involved in the designed algorithm to minimize side effects. In our experimental results, the GA-based PPDM approach is compared with traditional compact GA-based model and results clearly showed that our proposed method can obtain better performance with less computational cost.
2021-01-28
Zhang, M., Wei, T., Li, Z., Zhou, Z..  2020.  A service-oriented adaptive anonymity algorithm. 2020 39th Chinese Control Conference (CCC). :7626—7631.

Recently, a large amount of research studies aiming at the privacy-preserving data publishing have been conducted. We find that most K-anonymity algorithms fail to consider the characteristics of attribute values distribution in data and the contribution value differences in quasi-identifier attributes when service-oriented. In this paper, the importance of distribution characteristics of attribute values and the differences in contribution value of quasi-identifier attributes to anonymous results are illustrated. In order to maximize the utility of released data, a service-oriented adaptive anonymity algorithm is proposed. We establish a model of reaction dispersion degree to quantify the characteristics of attribute value distribution and introduce the concept of utility weight related to the contribution value of quasi-identifier attributes. The priority coefficient and the characterization coefficient of partition quality are defined to optimize selection strategies of dimension and splitting value in anonymity group partition process adaptively, which can reduce unnecessary information loss so as to further improve the utility of anonymized data. The rationality and validity of the algorithm are verified by theoretical analysis and multiple experiments.

2021-01-25
Arthy, R., Daniel, E., Maran, T. G., Praveen, M..  2020.  A Hybrid Secure Keyword Search Scheme in Encrypted Graph for Social Media Database. 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC). :1000–1004.

Privacy preservation is a challenging task with the huge amount of data that are available in social media. The data those are stored in the distributed environment or in cloud environment need to ensure confidentiality to data. In addition, representing the voluminous data is graph will be convenient to perform keyword search. The proposed work initially reads the data corresponding to social media and converts that into a graph. In order to prevent the data from the active attacks Advanced Encryption Standard algorithm is used to perform graph encryption. Later, search operation is done using two algorithms: kNK keyword search algorithm and top k nearest keyword search algorithm. The first scheme is used to fetch all the data corresponding to the keyword. The second scheme is used to fetch the nearest neighbor. This scheme increases the efficiency of the search process. Here shortest path algorithm is used to find the minimum distance. Now, based on the minimum value the results are produced. The proposed algorithm shows high performance for graph generation and searching and moderate performance for graph encryption.

2020-12-28
Cuzzocrea, A., Maio, V. De, Fadda, E..  2020.  Experimenting and Assessing a Distributed Privacy-Preserving OLAP over Big Data Framework: Principles, Practice, and Experiences. 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC). :1344—1350.
OLAP is an authoritative analytical tool in the emerging big data analytics context, with particular regards to the target distributed environments (e.g., Clouds). Here, privacy-preserving OLAP-based big data analytics is a critical topic, with several amenities in the context of innovative big data application scenarios like smart cities, social networks, bio-informatics, and so forth. The goal is that of providing privacy preservation during OLAP analysis tasks, with particular emphasis on the privacy of OLAP aggregates. Following this line of research, in this paper we provide a deep contribution on experimenting and assessing a state-of-the-art distributed privacy-preserving OLAP framework, named as SPPOLAP, whose main benefit is that of introducing a completely-novel privacy notion for OLAP data cubes.
2020-12-21
Liu, Q., Wu, W., Liu, Q., Huangy, Q..  2020.  T2DNS: A Third-Party DNS Service with Privacy Preservation and Trustworthiness. 2020 29th International Conference on Computer Communications and Networks (ICCCN). :1–11.
We design a third-party DNS service named T2DNS. T2DNS serves client DNS queries with the following features: protecting clients from channel and server attackers, providing trustworthiness proof to clients, being compatible with the existing Internet infrastructure, and introducing bounded overhead. T2DNS's privacy preservation is achieved by a hybrid protocol of encryption and obfuscation, and its service proxy is implemented on Intel SGX. We overcome the challenges of scaling the initialization process, bounding the obfuscation overhead, and tuning practical system parameters. We prototype T2DNS, and experiment results show that T2DNS is fully functional, has acceptable overhead in comparison with other solutions, and is scalable to the number of clients.
2020-12-02
Narang, S., Byali, M., Dayama, P., Pandit, V., Narahari, Y..  2019.  Design of Trusted B2B Market Platforms using Permissioned Blockchains and Game Theory. 2019 IEEE International Conference on Blockchain and Cryptocurrency (ICBC). :385—393.

Trusted collaboration satisfying the requirements of (a) adequate transparency and (b) preservation of privacy of business sensitive information is a key factor to ensure the success and adoption of online business-to-business (B2B) collaboration platforms. Our work proposes novel ways of stringing together game theoretic modeling, blockchain technology, and cryptographic techniques to build such a platform for B2B collaboration involving enterprise buyers and sellers who may be strategic. The B2B platform builds upon three ideas. The first is to use a permissioned blockchain with smart contracts as the technical infrastructure for building the platform. Second, the above smart contracts implement deep business logic which is derived using a rigorous analysis of a repeated game model of the strategic interactions between buyers and sellers to devise strategies to induce honest behavior from buyers and sellers. Third, we present a formal framework that captures the essential requirements for secure and private B2B collaboration, and, in this direction, we develop cryptographic regulation protocols that, in conjunction with the blockchain, help implement such a framework. We believe our work is an important first step in the direction of building a platform that enables B2B collaboration among strategic and competitive agents while maximizing social welfare and addressing the privacy concerns of the agents.

2020-11-02
Kadhim, H., Hatem, M. A..  2019.  Secure Data Packet in MANET Based Chaos-Modified AES Algorithm. 2019 2nd International Conference on Engineering Technology and its Applications (IICETA). :208–213.
Security is one of the more challenging problem for wireless Ad-Hoc networks specially in MANT due their features like dynamic topology, no centralized infrastructure, open architecture, etc. that make its more prone to different attacks. These attacks can be passive or active. The passive attack it hard to detect it in the network because its targets the confidential of data packet by eavesdropping on it. Therefore, the privacy preservation for data packets payload which it transmission over MANET has been a major part of concern. especially for safety-sensitive applications such as, privacy conference meetings, military applications, etc. In this paper it used symmetric cryptography to provide privacy for data packet by proposed modified AES based on five proposed which are: Key generation based on multi chaotic system, new SubByte, new ShiftRows, Add-two-XOR, Add-Shiftcycl.
2020-09-28
Sliwa, Benjamin, Haferkamp, Marcus, Al-Askary, Manar, Dorn, Dennis, Wietfeld, Christian.  2018.  A radio-fingerprinting-based vehicle classification system for intelligent traffic control in smart cities. 2018 Annual IEEE International Systems Conference (SysCon). :1–5.
The measurement and provision of precise and up-to-date traffic-related key performance indicators is a key element and crucial factor for intelligent traffic control systems in upcoming smart cities. The street network is considered as a highly-dynamic Cyber Physical System (CPS) where measured information forms the foundation for dynamic control methods aiming to optimize the overall system state. Apart from global system parameters like traffic flow and density, specific data, such as velocity of individual vehicles as well as vehicle type information, can be leveraged for highly sophisticated traffic control methods like dynamic type-specific lane assignments. Consequently, solutions for acquiring these kinds of information are required and have to comply with strict requirements ranging from accuracy over cost-efficiency to privacy preservation. In this paper, we present a system for classifying vehicles based on their radio-fingerprint. In contrast to other approaches, the proposed system is able to provide real-time capable and precise vehicle classification as well as cost-efficient installation and maintenance, privacy preservation and weather independence. The system performance in terms of accuracy and resource-efficiency is evaluated in the field using comprehensive measurements. Using a machine learning based approach, the resulting success ratio for classifying cars and trucks is above 99%.
Zhang, Shuaipeng, Liu, Hong.  2019.  Environment Aware Privacy-Preserving Authentication with Predictability for Medical Edge Computing. 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC). :90–96.
With the development of IoT, smart health has significantly improved the quality of people's life. A large amount of smart health monitoring system has been proposed, which provides an opportunity for timely and efficient diagnosis. Nevertheless, most of them ignored the impact of environment on patients' health. Due to the openness of the communication channel, data security and privacy preservation are crucial problems to be solved. In this work, an environment aware privacy-preserving authentication protocol based on the fuzzy extractor and elliptic curve cryptography (ecc) is designed for health monitoring system with mutual authentication and anonymity. Edge computing unit can authenticate all environmental sensors at one time. Fuzzy synthetic evaluation model is utilized to evaluate the environment equality with the patients' temporal health index (THI) as an assessment factor, which can help to predict the appropriate environment. The session key is established for secure communication based on the predicted result. Through security analysis, the proposed protocol can prevent common attacks. Moreover, performance analysis shows that the proposed protocol is applicable for resource-limited smart devices in edge computing health monitoring system.
2020-07-13
Andrew, J., Karthikeyan, J., Jebastin, Jeffy.  2019.  Privacy Preserving Big Data Publication On Cloud Using Mondrian Anonymization Techniques and Deep Neural Networks. 2019 5th International Conference on Advanced Computing Communication Systems (ICACCS). :722–727.

In recent trends, privacy preservation is the most predominant factor, on big data analytics and cloud computing. Every organization collects personal data from the users actively or passively. Publishing this data for research and other analytics without removing Personally Identifiable Information (PII) will lead to the privacy breach. Existing anonymization techniques are failing to maintain the balance between data privacy and data utility. In order to provide a trade-off between the privacy of the users and data utility, a Mondrian based k-anonymity approach is proposed. To protect the privacy of high-dimensional data Deep Neural Network (DNN) based framework is proposed. The experimental result shows that the proposed approach mitigates the information loss of the data without compromising privacy.

2020-04-20
Xiao, Tianrui, Khisti, Ashish.  2019.  Maximal Information Leakage based Privacy Preserving Data Disclosure Mechanisms. 2019 16th Canadian Workshop on Information Theory (CWIT). :1–6.
It is often necessary to disclose training data to the public domain, while protecting privacy of certain sensitive labels. We use information theoretic measures to develop such privacy preserving data disclosure mechanisms. Our mechanism involves perturbing the data vectors to strike a balance in the privacy-utility trade-off. We use maximal information leakage between the output data vector and the confidential label as our privacy metric. We first study the theoretical Bernoulli-Gaussian model and study the privacy-utility trade-off when only the mean of the Gaussian distributions can be perturbed. We show that the optimal solution is the same as the case when the utility is measured using probability of error at the adversary. We then consider an application of this framework to a data driven setting and provide an empirical approximation to the Sibson mutual information. By performing experiments on the MNIST and FERG data sets, we show that our proposed framework achieves equivalent or better privacy than previous methods based on mutual information.
Zhang, Xue, Yan, Wei Qi.  2018.  Comparative Evaluations of Privacy on Digital Images. 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). :1–6.
Privacy preservation on social networks is nowadays a societal issue. In this paper, our contributions are to establish such a model for privacy preservation. We use differential privacy for personal privacy analysis and measurement. Our conclusion is that privacy could be measured and preserved if the corresponding approaches could be taken.
Zhang, Xue, Yan, Wei Qi.  2018.  Comparative Evaluations of Privacy on Digital Images. 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). :1–6.
Privacy preservation on social networks is nowadays a societal issue. In this paper, our contributions are to establish such a model for privacy preservation. We use differential privacy for personal privacy analysis and measurement. Our conclusion is that privacy could be measured and preserved if the corresponding approaches could be taken.
2020-04-03
Sadique, Farhan, Bakhshaliyev, Khalid, Springer, Jeff, Sengupta, Shamik.  2019.  A System Architecture of Cybersecurity Information Exchange with Privacy (CYBEX-P). 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC). :0493—0498.
Rapid evolution of cyber threats and recent trends in the increasing number of cyber-attacks call for adopting robust and agile cybersecurity techniques. Cybersecurity information sharing is expected to play an effective role in detecting and defending against new attacks. However, reservations and or-ganizational policies centering the privacy of shared data have become major setbacks in large-scale collaboration in cyber defense. The situation is worsened by the fact that the benefits of cyber-information exchange are not realized unless many actors participate. In this paper, we argue that privacy preservation of shared threat data will motivate entities to share threat data. Accordingly, we propose a framework called CYBersecurity information EXchange with Privacy (CYBEX-P) to achieve this. CYBEX-P is a structured information sharing platform with integrating privacy-preserving mechanisms. We propose a complete system architecture for CYBEX-P that guarantees maximum security and privacy of data. CYBEX-P outlines the details of a cybersecurity information sharing platform. The adoption of blind processing, privacy preservation, and trusted computing paradigms make CYBEX-P a versatile and secure information exchange platform.
2020-01-21
Suksomboon, Kalika, Shen, Zhishu, Ueda, Kazuaki, Tagami, Atsushi.  2019.  C2P2: Content-Centric Privacy Platform for Privacy-Preserving Monitoring Services. 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC). 1:252–261.
Motivated by ubiquitous surveillance cameras in a smart city, a monitoring service can be provided to citizens. However, the rise of privacy concerns may disrupt this advanced service. Yet, the existing cloud-based services have not clearly proven that they can preserve Wth-privacy in which the relationship of three types of information, i.e., who requests the service, what the target is and where the camera is, does not leak. We address this problem by proposing a content-centric privacy platform (C2P2) that enables the construction of a Wth-privacy-preserving monitoring service without cloud dependency. C2P2 uses an image classification model of a target serving as the key to access the monitoring service specific to the target. In C2P2, communication is based on information-centric networking (ICN) that enables privacy preservation to be centered on the content itself rather than relying on a centralized system. Moreover, to preserve the privacy of bystanders, C2P2 separates the sensitive information (e.g., human faces) from the non-sensitive information (e.g., image background), while the privacy-aware forwarding strategies in C2P2 enable data aggregation and prevent privacy leakage resulting from false positive of image recognition. We evaluate the privacy leakage of C2P2 compared to that of the cloud-based system. The privacy analysis shows that, compared to the cloud-based system, C2P2 achieves a lower privacy loss ratio while reducing the communication cost significantly.
2020-01-20
Sui, Zhiyuan, de Meer, Hermann.  2019.  BAP: A Batch and Auditable Privacy Preservation Scheme for Demand-Response in Smart Grids. IEEE Transactions on Industrial Informatics. :1–1.
Advancing network technologies allows the setup of two-way communication links between energy providers and consumers. These developing technologies aim to enhance grid reliability and energy efficiency in smart grids. To achieve this goal, energy usage reports from consumers are required to be both trustworthy and confidential. In this paper, we construct a new data aggregation scheme in smart grids based on a homomorphic encryption algorithm. In the constructed scheme, obedient consumers who follow the instruction can prove its ajustment using a range proof protocol. Additionally, we propose a new identity-based signature algorithm in order to ensure authentication and integrity of the constructed scheme. By using this signature algorithm, usage reports are verified in real time. Extensive simulations demonstrate that our scheme outperforms other data aggregation schemes.
2019-11-18
Lu, Zhaojun, Wang, Qian, Qu, Gang, Liu, Zhenglin.  2018.  BARS: A Blockchain-Based Anonymous Reputation System for Trust Management in VANETs. 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :98–103.
The public key infrastructure (PKI) based authentication protocol provides the basic security services for vehicular ad-hoc networks (VANETs). However, trust and privacy are still open issues due to the unique characteristics of vehicles. It is crucial for VANETs to prevent internal vehicles from broadcasting forged messages while simultaneously protecting the privacy of each vehicle against tracking attacks. In this paper, we propose a blockchain-based anonymous reputation system (BARS) to break the linkability between real identities and public keys to preserve privacy. The certificate and revocation transparency is implemented efficiently using two blockchains. We design a trust model to improve the trustworthiness of messages relying on the reputation of the sender based on both direct historical interactions and indirect opinions about the sender. Experiments are conducted to evaluate BARS in terms of security and performance and the results show that BARS is able to establish distributed trust management, while protecting the privacy of vehicles.
2019-03-28
Wen, M., Yao, D., Li, B., Lu, R..  2018.  State Estimation Based Energy Theft Detection Scheme with Privacy Preservation in Smart Grid. 2018 IEEE International Conference on Communications (ICC). :1-6.

The increasing deployment of smart meters at individual households has significantly improved people's experience in electricity bill payments and energy savings. It is, however, still challenging to guarantee the accurate detection of attacked meters' behaviors as well as the effective preservation of users'privacy information. In addition, rare existing research studies jointly consider both these two aspects. In this paper, we propose a Privacy-Preserving energy Theft Detection scheme (PPTD) to address the energy theft behaviors and information privacy issues in smart grid. Specifically, we use a recursive filter based on state estimation to estimate the user's energy consumption, and detect the abnormal data. During data transmission, we use the lightweight NTRU algorithm to encrypt the user's data to achieve privacy preservation. Security analysis demonstrates that in the PPTD scheme, only authorized units can transmit/receive data, and data privacy are also preserved. The performance evaluation results illustrate that our PPTD scheme can significantly reduce the communication and computation costs, and effectively detect abnormal users.

Bagri, D., Rathore, S. K..  2018.  Research Issues Based on Comparative Work Related to Data Security and Privacy Preservation in Smart Grid. 2018 4th International Conference on Computing Sciences (ICCS). :88-91.

With the advancement of Technology, the existing electric grids are shifting towards smart grid. The smart grids are meant to be effective in power management, secure and safe in communication and more importantly, it is favourable to the environment. The smart grid is having huge architecture it includes various stakeholders that encounter challenges in the name of authorisation and authentication. The smart grid has another important issue to deal with that is securing the communication from varieties of cyber-attacks. In this paper, we first discussed about the challenges in the smart grid data communication and later we surveyed the existing cryptographic algorithm and presented comparative work on certain factors for existing working cryptographic algorithms This work gives insight conclusion to improve the working scheme for data security and Privacy preservation of customer who is one of the stack holders. Finally, with the comparative work, we suggest a direction of future work on improvement of working algorithms for secure and safe data communication in a smart grid.

2019-02-18
Afsharinejad, Armita, Hurley, Neil.  2018.  Performance Analysis of a Privacy Constrained kNN Recommendation Using Data Sketches. Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. :10–18.
This paper evaluates two algorithms, BLIP and JLT, for creating differentially private data sketches of user profiles, in terms of their ability to protect a kNN collaborative filtering algorithm from an inference attack by third-parties. The transformed user profiles are employed in a user-based top-N collaborative filtering system. For the first time, a theoretical analysis of the BLIP is carried out, to derive expressions that relate its parameters to its performance. This allows the two techniques to be fairly compared. The impact of deploying these approaches on the utility of the system—its ability to make good recommendations, and on its privacy level—the ability of third-parties to make inferences about the underlying user preferences, is examined. An active inference attack is evaluated, that consists of the injection of a number of tailored sybil profiles into the system database. User profile data of targeted users is then inferred from the recommendations made to the sybils. Although the differentially private sketches are designed to allow the transformed user profiles to be published without compromising privacy, the attack we examine does not use such information and depends only on some pre-existing knowledge of some user preferences as well as the neighbourhood size of the kNN algorithm. Our analysis therefore assesses in practical terms a relatively weak privacy attack, which is extremely simple to apply in systems that allow low-cost generation of sybils. We find that, for a given differential privacy level, the BLIP injects less noise into the system, but for a given level of noise, the JLT offers a more compact representation.
2019-01-31
Nakamura, T., Nishi, H..  2018.  TMk-Anonymity: Perturbation-Based Data Anonymization Method for Improving Effectiveness of Secondary Use. IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society. :3138–3143.

The recent emergence of smartphones, cloud computing, and the Internet of Things has brought about the explosion of data creation. By collating and merging these enormous data with other information, services that use information become more sophisticated and advanced. However, at the same time, the consideration of privacy violations caused by such merging is indispensable. Various anonymization methods have been proposed to preserve privacy. The conventional perturbation-based anonymization method of location data adds comparatively larger noise, and the larger noise makes it difficult to utilize the data effectively for secondary use. In this research, to solve these problems, we first clarified the definition of privacy preservation and then propose TMk-anonymity according to the definition.

2018-09-28
Lu, Z., Shen, H..  2017.  A New Lower Bound of Privacy Budget for Distributed Differential Privacy. 2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT). :25–32.

Distributed data aggregation via summation (counting) helped us to learn the insights behind the raw data. However, such computing suffered from a high privacy risk of malicious collusion attacks. That is, the colluding adversaries infer a victim's privacy from the gaps between the aggregation outputs and their source data. Among the solutions against such collusion attacks, Distributed Differential Privacy (DDP) shows a significant effect of privacy preservation. Specifically, a DDP scheme guarantees the global differential privacy (the presence or absence of any data curator barely impacts the aggregation outputs) by ensuring local differential privacy at the end of each data curator. To guarantee an overall privacy performance of a distributed data aggregation system against malicious collusion attacks, part of the existing work on such DDP scheme aim to provide an estimated lower bound of privacy budget for the global differential privacy. However, there are two main problems: low data utility from using a large global function sensitivity; unknown privacy guarantee when the aggregation sensitivity of the whole system is less than the sum of the data curator's aggregation sensitivity. To address these problems while ensuring distributed differential privacy, we provide a new lower bound of privacy budget, which works with an unconditional aggregation sensitivity of the whole distributed system. Moreover, we study the performance of our privacy bound in different scenarios of data updates. Both theoretical and experimental evaluations show that our privacy bound offers better global privacy performance than the existing work.