Achieve Privacy Protection Research Articles

Legitimacy of Personal Data Mining and Related Regulatory Approaches: China’s Choices

Personal data mining is a crucial but controversial topic in the big data era. It is widely accepted in the technical filed as a big data technology. While more and more people begin to question its legitimacy for it causes many social and legal problems. This paper tries to demonstrate the legitimacy of personal data mining from the following reasons: a) personal data has certain public goods attribute and sharing characteristic, which is the internal premise of personal data mining; b) data appreciation theory is the theoretical basis of personal data mining; c) the principle of interests balance is the logical requirement of personal data mining; d) big data sharing economy provides practical soil for personal data mining. Based on the legitimacy analysis and China’s data development practices, personal data mining should be encouraged. Meanwhile, it is particularly important to regulate the approaches of personal data mining in four aspects: a) establishing the purpose of combining personal data mining and protection; b) balancing the interests of all parties in the process of personal data mining; c) improving data classification legislation; d) innovating technical means to achieve privacy protection.

Reversible data hiding in encrypted images with public key cryptography: a review of its benefits and open issues

Reversible data hiding is a new research direction of data hiding technology. In particular with the adoption of cloud services, reversible data hiding in encrypted images is a well-known notion of achieving privacy protection in the cloud environment. According to the encryption method, it can be classified into symmetric encryption and asymmetric encryption. Compared with symmetric encryption, asymmetric encryption system allows homomorphic addition and homomorphic multiplication to be performed in the ciphertext domain, which makes the information hiding process more secure and the content is more effectively protected. However, there are some problems in existing schemes. The aim of this paper is to analysis recent issues regarding reversible data hiding in encrypted images with public key cryptography, and offer valuable solutions to the aforementioned issues, including feasible theoretical basis. Finally, focused on the technology needs and difficulties, several future directions in this field are proposed.

Blockchain-Based Personal Health Records Sharing Scheme With Data Integrity Verifiable

The sharing of personal health records can help to improve the accuracy of the doctor's diagnosis and to promote the progress of medical research. Currently, to reduce the maintenance cost of data, personal health records are usually outsourced to a third party such as the cloud service provider. In this case, patients may lose direct control over their personal health records and the semi-trusted cloud service provider may tamper with or reveal personal health records. Therefore, ensuring the privacy and integrity of personal health records and realizing the fine-grained access control are crucial issues when personal health records are shared. As a distributed architecture with decentralized and tamper-proof features, blockchain provides a new way to protect the personal health records sharing system. In this paper, we propose a new personal health records sharing scheme with data integrity verifiable based on blockchain. Aiming at the problems of privacy disclosure, limited keyword search ability and loss of control rights in the process of personal health record sharing, the new scheme uses searchable symmetric encryption and attribute-based encryption techniques to achieve privacy protection, keyword search, and fine-grained access control. Compared with the existing similar schemes, the new scheme allows patients to distribute attribute private key for users, avoiding many security problems caused by the existing of attribute authority in the scheme. Furthermore, the new scheme uses blockchain to manage keys in the scheme, avoiding the single point failure problem of centralized key management. In particular, the new scheme stores the hash values of encrypted personal health records in blockchain, and the related index set is stored in smart contract, which can further improve the efficiency of data integrity verification. Finally, performance evaluation and security analysis indicate that our scheme is secure and feasible for practical use.

Improving k-anonymity based privacy preservation for collaborative filtering

Collaborative Filtering (CF) is applied in recommender systems to predict users’ preference through filtering the information or patterns. Privacy Preserving Collaborative Filtering (PPCF) aims to achieve privacy protection in the recommendation process, which has an increasing significance in recommender systems and thus attracted much interests in recent years. Existing PPCF methods are mainly based on cryptography, obfuscation, perturbation and differential privacy. They have high computational cost, low data quality and difficulties in calibrating the magnitude of noise. This paper proposes a (p, l, α)-diversity method that improves the existing k-anonymity method in PPCF, where p is attacker’s prior knowledge about users’ ratings and (l, α) is the diversity among users in each group to improve the level of privacy preserving. To achieve (l, α)-diversity, users in each equivalence class shall come from at least l (l < k) clusters in α clustering results. Therefore, we firstly apply Latent Factor Model (LFM) to reduce matrix sparsity. Then we propose an improved Maximum Distance to Average Vector (MDAV) microaggregation algorithm based on importance partitioning to increase the homogeneity among the records in each group which can retain better data quality in (p, l, α)-diversity model. Finally, we apply t-closeness in PPCF. Theoretical analysis and experimental results demonstrate our approach assures a higher level of privacy preserving and less information loss than existing methods.

A criterion for privacy protection in data collection and its attainment via randomized response procedures

Randomized response (RR) methods have long been suggested for protecting respondents’ privacy in statistical surveys. However, how to set and achieve privacy protection goals have received little attention. We give a full development and analysis of the view that a privacy mechanism should ensure that no intruder would gain much new information about any respondent from his response. Formally, we say that a privacy breach occurs when an intruder’s prior and posterior probabilities about a property of a respondent, denoted $p$ and $p_{*}$, respectively, satisfy $p_{*} h_{u}(p)$, where $h_{l}$ and $h_{u}$ are two given functions. An RR procedure protects privacy if it does not permit any privacy breach. We explore effects of $(h_{l},h_{u})$ on the resultant privacy demand, and prove that it is precisely attainable only for certain $(h_{l},h_{u})$. This result is used to define a canonical strict privacy protection criterion, and give practical guidance on the choice of $(h_{l},h_{u})$. Then, we characterize all privacy satisfying RR procedures and compare their effects on data utility using sufficiency of experiments and identify the class of all admissible procedures. Finally, we establish an optimality property of a commonly used RR method.

Privacy preserving method based on Voronoi diagram in mobile crowd computing

With the development of mobile smartphones equipped with a large number of sensor devices, mobile crowd computing has become the hotspot of current research. In the application, the publishers use the application platform to release the task and then select the appropriate users to participate in the task by bidding and collect their data, in which the users’ identity, location, and other private information face the risk of disclosure. To solve the problem of privacy disclosure in mobile crowd computing, we propose a privacy preserving method based on Voronoi Polygon (PP - Voronoi). The users construct the Voronoi diagram to form a cloaked region hiding users identity and then calculate the anchor point of the cloaked region, by which the users communicate with the task publisher to achieve privacy protection of the cloaked region and K anonymity. The experimental results show that compared with other privacy preserving methods, PP-Voronoi method has better effectiveness of privacy protection and lower network traffic.

Achieving Privacy Protection Using Distributed Load Scheduling: A Randomized Approach

With the development of smart grid technology, smart meters have been widely used in various applications, which also incurs security and privacy issues in the meanwhile. With high-precision smart meter data, the residential users’ living habits or behavior patterns can be possibly identified for malicious use. To protect the users’ privacy, most existing works try to flatten or randomize the aggregated load profile only using rechargeable batteries through centralized control strategies, which may be inefficient and economically costly. In this paper, we propose a general framework to minimize a weighted sum of privacy leakage, electricity bill, and the user experience sacrifice by exploring the capacity of all end-devices of residential users including three categories of shiftable devices, i.e., thermostatically controlled devices, rechargeable batteries, and successive operation devices. By adopting the randomized approach, we propose distributed algorithms to solve the optimization problem efficiently for two scenarios where the user energy is able to feed back to the grid or not. We show that the algorithms can converge to near-optimal solutions with rigorous proofs. Extensive simulations are conducted to demonstrate the effectiveness of proposed algorithms.

Efficient Location Privacy-Preserving k-Anonymity Method Based on the Credible Chain

Currently, although prevalent location privacy methods based on k-anonymizing spatial regions (K-ASRs) can achieve privacy protection by sacrificing the quality of service (QoS), users cannot obtain accurate query results. To address this problem, it proposes a new location privacy-preserving k-anonymity method based on the credible chain with two major features. First, the optimal k value for the current user is determined according to the user’s environment and social attributes. Second, rather than forming an anonymizing spatial region (ASR), the trusted third party (TTP) generates a fake trajectory that contains k location nodes based on properties of the credible chain. In addition, location-based services (LBS) queries are conducted based on the trajectory, and privacy level is evaluated by instancing θ privacy. Simulation results and experimental analysis demonstrate the effectiveness and availability of the proposed method. Compared with methods based on ASR, the proposed method guarantees 100% QoS.

V2X security: A case study of anonymous authentication

Vehicle to Everything (V2X) is the enabling technique for future connected cars. Security is essential to the V2X technology, and privacy is an integral element of V2X security. It has been recognized that privacy preserving/anonymous authentication is a fundamental step towards achieving privacy protection in V2X. We are thus motivated to study anonymous authentication, as a case study of V2X security. Specifically we propose two lightweight anonymous credential schemes for anonymous authentication in V2X: one is under the rationale of “witness update outsourcing”, and is applicable to V2V; the other is suitable for V2I, with the characteristic that a vehicular OBU is enabled to operate entirely on the compact bilinear group. We conduct performance analysis both analytically and empirically, and get promising results.

Multiagent Reinforcement Learning With Sparse Interactions by Negotiation and Knowledge Transfer.

Reinforcement learning has significant applications for multiagent systems, especially in unknown dynamic environments. However, most multiagent reinforcement learning (MARL) algorithms suffer from such problems as exponential computation complexity in the joint state-action space, which makes it difficult to scale up to realistic multiagent problems. In this paper, a novel algorithm named negotiation-based MARL with sparse interactions (NegoSIs) is presented. In contrast to traditional sparse-interaction-based MARL algorithms, NegoSI adopts the equilibrium concept and makes it possible for agents to select the nonstrict equilibrium-dominating strategy profile (nonstrict EDSP) or meta equilibrium for their joint actions. The presented NegoSI algorithm consists of four parts: 1) the equilibrium-based framework for sparse interactions; 2) the negotiation for the equilibrium set; 3) the minimum variance method for selecting one joint action; and 4) the knowledge transfer of local Q -values. In this integrated algorithm, three techniques, i.e., unshared value functions, equilibrium solutions, and sparse interactions are adopted to achieve privacy protection, better coordination and lower computational complexity, respectively. To evaluate the performance of the presented NegoSI algorithm, two groups of experiments are carried out regarding three criteria: 1) steps of each episode; 2) rewards of each episode; and 3) average runtime. The first group of experiments is conducted using six grid world games and shows fast convergence and high scalability of the presented algorithm. Then in the second group of experiments NegoSI is applied to an intelligent warehouse problem and simulated results demonstrate the effectiveness of the presented NegoSI algorithm compared with other state-of-the-art MARL algorithms.

A Novel Biometrics-based One-Time Commitment Authenticated Key Agreement Scheme with Privacy Protection for Mobile Network

In recent years, due to the wide applications of social network and electronic business, privacy protection in the cyber world has attracted much attention. And in general, in order to solve the problems to set up a secure channel over public Internet, authenticated key agreement protocols can be adopted because it can achieve authentication of the corresponding participants and confidentiality of data transmission at the same time. Next, many authenticated key agreement protocols use various functional algorithms, such as dynamic identity and chaotic maps to achieve privacy protection. In this paper, we firstly put forward a new method to solve privacy protection problem, called One-Time Commitment, which is more efficient than One-Time Password. Then a new robust biometrics-based authenticated key agreement protocol with privacy protection using interactive hashing is given for mobile network. Our protocol has the feature of high-efficient and user friendly at the same time. Security of the protocol is based on the biometric authentication, a secure one way hash function and a pair of secure interactive hashing. Moreover the proposed protocol can not only refrain from many consuming algorithms, such as modular exponential computing, scalar multiplication on an elliptic curve, and even symmetric encryption, but is also robust to many kinds of attacks, such as replay attack, perfect forward secrecy and so on. Finally, we provide the secure proof and the efficiency analysis about our proposed scheme.

A Novel and Provable Authenticated Key Agreement Protocol with Privacy Protection Based on Chaotic Maps towards Mobile Network

Key agreement is a crucial cryptographic primitive for building secure communication channels between two parties in a network. In the research literature a typical protocol aims for key secrecy and mutual authentication. However, there are many important practical scenarios where privacy protection is more desirable, especially for social network. Network privacy security means that the personal data and online data are not peep, intrusion, interference, illegal collection and utilization. In our paper, we propose a robust chaotic maps-based authentication key agreement scheme with privacy protection using smart cards. The key idea of our proposed scheme is to adopt chaotic maps for mutual authentication, not to encrypt/decrypt messages transferred between user and server, which can make our proposed scheme much more efficient. Next, we give the formal provable security under the random oracle model for our scheme. Finally, our proposed scheme can not only achieve privacy protection, but also avoid time-consuming modular exponentiation and scalar multiplication on elliptic curves. Meanwhile, it can resist various common attacks, and provide prefect forward secrecy and known-key secrecy. In brief, compared with related schemes, our proposed scheme is more secure, effective and practical.

Location-Based Services and Privacy Protection under Mobile Cloud Computing

Location-based services can provide personalized services based on location information of moving objects and have already been widely used in public safety services, transportation, entertainment and many other areas. With the rapid development of mobile communication technology and popularization of intelligent terminals, there will be great commercial prospects to provide location-based services under mobile cloud computing environment. However, the high adhesion degree of mobile terminals to users not only brings facility but also results in the risk of privacy leak. The paper introduced the necessities and advantages to provide location-based services under mobile cloud computing environment, stressed the importance to protect location privacy in LBS services, pointed out new security risks brought by mobile cloud computing, and proposed a new framework and implementation method of LBS service. The cloud-based LBS system proposed in this paper is able to achieve privacy protection from the confidentiality of outsourced data and integrity of service results, and can be used as a reference while developing LBS system under mobile cloud computing environment.

An Efficient Revocable Group Signature Scheme in Vehicular Ad Hoc Networks

Although many revocable group signature schemes has been proposed in vehicular ad hoc networks (VANETs), the existing schemes suffer from long computation delay on revocation that they cannot adapt to the dynamic VANETs. Based on Chinese remainder theorem and Schnorr signature algorithm, this paper proposes an efficient revocable group signature scheme in VANETs. In the proposed scheme, it only need to update the corresponding group public key when a member quits the group, and in the meanwhile the key pairs of unchanged group members are not influenced. Furthermore, this scheme can achieve privacy protection by making use of blind certificates. Before joining to the VANETs, users register at local trusted agencies (LTAs) with their ID cards to obtain blind certificates. The blind certificate will be submitted to road-side units (RSUs) to verify the legality of users. Thus, the real identities of users can be protected. In addition, if there is a dispute, users can combine to submit open applications to RSUs against a disputed member. And LTAs can determine the real identity of the disputed member. Moreover, since the key pairs employed by a user are different in different groups, attackers are not able to track the movement of users with the obtained public keys in a group. Furthermore, performance analysis shows that proposed scheme has less computation cost than existing schemes.

Secured Personalized Web Search usingOntology and Hashing Technique

Personalized Web Search is a best way to improve search quality by customizing search results for the users with individual information goals. This is achieved through implicitly collection of user profiles, browsing history, clicked through data, bookmarks, location of the user. However, users are not willing to expose their private preference information to search engines. Many research are undergoing in improving Personalized Web Search (PWS), this system added another dimension that is Privacy Protection in PWS. This system has a framework by name personalized privacy preserving search framework, which is very helpful framework where eavesdroppers are not able to get any details of the users since privacy measures are taking place at client side. To achieve privacy protection, this system provides metadata and user?s query encryption which is achieved using MD-5 hashing technique. In the beginning it will look like a keyword search but there is a behavioral observing system called the Spy-NB, which monitors the users? behavior and which will provide the results sets according to the users? interest in efficient way. To improve the personalized search, this system uses Taxonomy (ontology) concepts, RSMV, GreedyIL & GreedyDP algorithm. This system may be applied in real world applications like Google and Yahoo search engine, which allows users to describe their interests explicitly by selecting from pre-defined preference option, so that the results that match are re-rank according to the user?s interests and also gives users the option to save web sites they like and block those they dislike.

Effective reversible data hiding in encrypted image with privacy protection for image content

In this paper, we propose a novel reversible data hiding scheme in encrypted image. The content owner encrypts the original image with the encryption key to achieve privacy protection for image content, and then, each block of the encrypted image is embedded with one secret bit by the data hider using the data-hiding key. Through the elaborate selection for partial pixels to be flipped, data hiding process only conducts slighter modifications to each block, which leads to significant improvement of visual quality for the decrypted image. The receiver can easily decrypt the marked, encrypted image using the encryption key, and then, through the data-hiding key and an adaptive evaluation function of smoothness characteristic along the isophote direction, secret data can be extracted from the decrypted image, and the original image can further be recovered successfully. Experimental results demonstrate the effectiveness of the proposed scheme.

Biometric template protection for speaker recognition based on universal background models

(Voice-) biometric data is considered as personally identifiable information, that is, the increasing demand on (mobile) speaker recognition systems calls for applications which prevent from privacy threats, such as identity-theft or tracking without consent. Technologies of biometric template protection, in particular biometric cryptosystems, fulfil standardised properties of irreversibility and unlinkability which represent appropriate countermeasures to such vulnerabilities of conventional biometric recognition systems. Thereby, public confidence in and social acceptance of biometric applications is strengthened. In this work the authors propose a binarisation technique, which is used to extract scalable high-entropy binary voice reference data (templates) from speaker models, based on Gaussian mixture models and universal background models. Binary feature vectors are then protected within a template protection scheme in particular, fuzzy commitment scheme, in which error correction list-decoding is employed to overcome high intra-class variance of voice samples. In experiments, which are evaluated out on a text-independent speaker corpus of 339 individuals, it is demonstrated that the fully ISO/IEC IS 24745 compliant system achieves privacy protection at a negligible loss of biometric performance, confirming the soundness of the presented approach.

Robust elliptic curve cryptography‐based three factor user authentication providing privacy of biometric data

Recently, to achieve privacy protection using biometrics, Fan and Lin proposed a three-factor authentication scheme based on password, smart card and biometrics. However, the authors have found that Fan and Lin's proposed scheme (i) has flaws in the design of biometrics privacy, (ii) fails to maintain a verification table, making it vulnerable to stolen-verifier attack and modification attack, and (iii) is vulnerable to insider attacks. Thus, the authors propose an elliptic curve cryptography-based authentication scheme that is improved with regard to security requirements. The authors’ proposed scheme overcomes the flaws of Fan and Lin's scheme and is secured from attacks. Furthermore, the authors have presented a security analysis of their scheme to show that their scheme is suitable for the biometric systems.

A Distributed Pseudonym Management Scheme in VANETs

Security and privacy have been important issues in VANETs. Anonymity is an effective way to achieve privacy protection, and it sometimes requires to be disclosed for determining traffic liability. In most pseudonym schemes, an authority is aware of a vehicle's secret, and its compromise will result in the leakage of a large amount of privacy information. So, we propose a distributed traceable pseudonym management scheme in VANETs. In the scheme, a blind signature method is adopted to achieve strict separation of issuance and tracking. The distributed tracking protocol is proposed to enhance the robustness for tracking, which is based on the improved scheme for shared generation of RSA keys. An efficient pseudonymous authentication mechanism is proposed to reduce the communication overhead. Compared with other related proposals, our scheme is unforgeability, especially against authority forge attacks, and has better robustness. Moreover, the performance analysis shows that it is efficient in VANETs.

Developing accountability-based solutions for data privacy in the cloud

Contemporary developments in information and communication technologies make “command and control”-based legislation, for example, EU Directive 95/46/EC, a decreasingly credible means of providing appropriate privacy protection for personally identifiable information (PII) across wide areas of commercial activity. Current legislation provides limited transparency and oversight, fails to encourage privacy innovation, and lacks the flexibility to effectively and efficiently regulate new technologies and globalized business practices. There is a growing consensus that regional, and ultimately global, data privacy regimes will have to adopt new regulatory approaches, and that such approaches should incorporate, as a key element, a greater focus on “accountability”. This raises two key questions: what is meant by “accountability”, and how might it translate into practical mechanisms for achieving privacy protection? This article considers these questions in the context of “cloud computing”, examining how procedural and technical solutions can be co-designed to demonstrate accountability in order to resolve jurisdictional privacy and security risks within the cloud.