Leak Users Research Articles

Ciphertext-Policy Attribute-Based Encryption for Cloud Storage: Toward Data Privacy and Authentication in AI-Enabled IoT System

People can store their data on servers in cloud computing and allow public users to access data via data centers. One of the most difficult tasks is to provide security for the access policy of data, which is also needed to be stored at cloud servers. The access structure (policy) itself may reveal partial information about what the ciphertext contains. To provide security for the access policy of data, a number of encryption schemes are available. Among these, CP-ABE (Ciphertext-Policy Attribute-Based Encryption) scheme is very significant because it helps to protect, broadcast, and control the access of information. The access policy that is sent as plaintext in the existing CP-ABE scheme along with a ciphertext may leak user privacy and data privacy. To resolve this problem, we hereby introduce a new technique, which hides the access policy using a hashing algorithm and provides security against insider attack using a signature verification scheme. The proposed system is compared with existing CP-ABE schemes in terms of computation and expressive policies. In addition, we can test the functioning of any access control that could be implemented in the Internet of Things (IoT). Additionally, security against indistinguishable adaptive chosen ciphertext attacks is also analyzed for the proposed work.

MPrivacy: A Privacy Policy Engine and Safeguard Mechanism in Mobile Devices

Within the scope of mobile privacy, there are many attack methods that can leak users’ private information. The communication between applications can be used to violate permissions and access private information without asking for the user’s authorization. Hence, many researchers made protection mechanisms against privilege escalation. However, attackers can further utilize inference algorithms to derive new information out of available data or improve the information quality without violating privilege limits. In this work. we describe the notion of Information Escalation Attack and propose a detection and protection mechanism using Inference Graph and Policy Engine for the user to control their policy on the App’s privilege in information escalation. Our implementation results show that the proposed privacy protection service is feasible and provides good useability.

Private Data Aggregation Based on Fog-Assisted Authentication for Mobile Crowd Sensing

In mobile crowd sensing (MCS), the cloud as a single sensing platform undertakes a large number of communication tasks, leading to the reduction of sensing task execution efficiency and the risk of loss and leakage of users’ private data. In this paper, we propose a spatial ciphertext aggregation scheme with collaborative verification of fog nodes. Firstly, the cloud and fog collaboration architecture is constructed. Fog nodes are introduced for data validation and slices transmission, reducing computing cost on the sensing platform. Secondly, a multipath transmission method of slice data is proposed, in which the user identity and data are transmitted anonymously by the secret sharing method, and the data integrity is guaranteed by hash chain authentication. Finally, a spatial data aggregation method based on privacy protection is presented. The ciphertext aggregation calculation of the sensing platform is realized through Paillier homomorphic encryption, and the problem of insufficient data coverage in the sensing region is solved by the position-based weight interpolation method. The security analysis demonstrates that the scheme can achieve the expected security goal. The simulation results show the feasibility and effectiveness of the proposed scheme.

Secure Federated Matrix Factorization

To protect user privacy and meet law regulations, federated (machine) learning is obtaining vast interests in recent years. The key principle of federated learning is training a machine learning model without needing to know each user’s personal raw private data. In this article, we propose a secure matrix factorization framework under the federated learning setting, called FedMF. First, we design a user-level distributed matrix factorization framework where the model can be learned when each user only uploads the gradient information (instead of the raw preference data) to the server. While gradient information seems secure, we prove that it could still leak users’ raw data. To this end, we enhance the distributed matrix factorization framework with homomorphic encryption. We implement the prototype of FedMF and test it with a real movie rating dataset. Results verify the feasibility of FedMF. We also discuss the challenges for applying FedMF in practice for future research.

Location-Privacy Leakage and Integrated Solutions for 5G Cellular Networks and Beyond.

The fifth generation (5G) of cellular networks improves the precision of user localization and provides the means to disclose location information to over-the-top (OTT) service providers. The network data analytics function (NWDAF) can further elaborate this information at an aggregated level using artificial intelligence techniques. These powerful features may lead to the improper use of user location information by mobile network operators (MNOs) and OTT service providers. Moreover, vulnerabilities at various layers may also leak user location information to eavesdroppers. Hence, the privacy of users is likely at risk, as location is part of their sensitive data. In this paper, we first go through the evolution of localization in cellular networks and investigate their effects on location privacy. Then, we propose a location-privacy-preserving integrated solution comprising virtual private mobile networks, an independent authentication and billing authority, and functions to protect wireless signals against location information leakage. Moreover, we advocate the continuous and detailed control of localization services by the user.

Differentially private and utility-aware publication of trajectory data

Trajectory data is valuable for various applications, especially for intelligent transportation systems, which hunger for plenty of trajectories. However, publishing trajectory data while respecting users’ privacy has been a long-standing challenge. Currently, the prevailing releasing solutions usually merge trajectory locations based on k-means and add unbounded noise with Laplace distribution to the count of trajectory to achieve differential privacy protection. The trajectory merging methods based on k-means have a low efficiency and unbounded noise with Laplace distribution will leak user’ privacy and suffer from serious utility loss. To solve the above two problems, we devise two differentially private and utility-aware publication methods of trajectory data. More specifically, we first propose two trajectory merging schemes based on k-means || clustering. The first one is to use k-means || clustering algorithm to cluster the location area, and all the points in the cluster are replaced by the center of cluster. The other is to utilize Staircase mechanism to perturb the cluster centers in order to improve the level of privacy protection. Afterwards, we propose a bounded Staircase noise generation algorithm to perturb the true count of generalized trajectories. We prove our proposed methods preserve differential privacy theoretically. Experimental comparison show that our proposed publication methods significantly outperform existing approaches in terms of data utility and efficiency.

Automated Multi-Layered Bytecode Generation for Preventing Sensitive Information Leaks From Android Applications

Sensitive information leakages from applications are a critical issue in the Android ecosystem. Despite the advance of techniques to secure applications such as packing and obfuscation, a lot of applications are still under the threat of repackaging attacks that inject malicious code and re-distribute applications. Also, as we are becoming more dependent on mobile technologies, more sensitive information is used on our mobile devices. Hence, it is of great importance to reduce the risk of such sensitive information leaks. In this paper, we first present a threat model that attempts to leak users’ sensitive information by using the repackaging attack, named ReMaCi attack. By analyzing the top 8,546 applications downloaded from Google Play Store, we show that 50% of them are really vulnerable to the ReMaCi attack. We, thus, propose a novel, automated static anti-analysis tool, called AmpDroid, for preventing sensitive information leaks. AmpDroid identifies sensitive dataflows and isolates the code that handles the sensitive data from an application. To demonstrate the effectiveness of AmpDroid, we perform the security and performance evaluation of AmpDroid, comparing it with other obfuscation tools.

A Secure and Policy-Controlled Signature Scheme With Strong Expressiveness and Privacy-Preserving Policy

The policy-controlled signature (PCS) scheme uses the access policy to control signature verification permission. However public access policy that may contain private information will leak user privacy. At the same time, the expressiveness of access structures in the PCS schemes is weak. Therefore, we propose a policy-controlled signature scheme with strong expressiveness and privacy-preserving policy (PCS-PP), in which linear secret sharing schemes is to design access structure which has strong expression, the three primes composite order bilinear groups is used to hide the attribute value into the attribute name that may expose the privacy data by data distortion concept. The proposed PCS-PP scheme not only has correctness and privacy-preserving policy, but also supports fine-grained signature verification. In addition, the unforgeability is proved in the random oracle model. Compared to the related schemes, the proposed PCS-PP scheme has superiority in features, computation cost and storage.

Privacy-preserving image multi-classification deep learning model in robot system of industrial IoT

Deep learning in robot systems is a popular application that can learn and train the results per requirements, but that collects sensitive information in the training process, easily causing leakage of users’ private information. To date, privacy-preserving deep learning models in robot systems have been sparsely researched. To solve the privacy leakage problem of deep learning in robot systems and fill the gap in robotics deep learning privacy research, in this paper a novel privacy-preserving image multi-classification deep-learning (PIDL) model in robot systems is presented. In PIDL, two schemes are proposed that adopt two groups of encrypted activation and cost functions—sigmoid plus cross-entropy function (PIDLSC) and softmax plus log-likelihood function (PIDLSL)—with secure calculation protocols, which are applied in a fog control center (FCC) with a non-colluding honest server by homomorphic encryption to improve the training efficiency, solve the encryption computation questions, and protect data and model privacy in robot systems. Security analysis and performance evaluation demonstrate that the proposed schemes realize security, correctness, and efficiency with low communication and computational costs.

Tracking sex: The implications of widespread sexual data leakage and tracking on porn websites

This article explores tracking and privacy risks on pornography websites. Our analysis of 22,484 pornography websites indicated that 93% leak user data to a third-party. Tracking on these sites is highly concentrated by a handful of major companies, which we identify. We successfully extracted privacy policies for 3856 sites, 17% of the total. The policies were written such that one might need a 2-year college education to understand them. Our content analysis of the sample’s domains indicated 44.97% of them expose or suggest a specific gender/sexual identity or interest likely to be linked to the user. We identify three core implications of the quantitative results: (1) the unique/elevated risks of porn data leakage versus other types of data, (2) the particular risks/impact for “vulnerable” populations, and (3) the complications of providing consent for porn site users and the need for affirmative consent in these online sexual interactions.

Fine-Grained Privacy Detection with Graph-Regularized Hierarchical Attentive Representation Learning

Due to the complex and dynamic environment of social media, user generated contents (UGCs) may inadvertently leak users’ personal aspects, such as the personal attributes, relationships and even the health condition, and thus place users at high privacy risks. Limited research efforts, thus far, have been dedicated to the privacy detection from users’ unstructured data (i.e., UGCs). Moreover, existing efforts mainly focus on applying conventional machine learning techniques directly to traditional hand-crafted privacy-oriented features, ignoring the powerful representing capability of the advanced neural networks. In light of this, in this article, we present a fine-grained privacy detection network (GrHA) equipped with graph-regularized hierarchical attentive representation learning. In particular, the proposed GrHA explores the semantic correlations among personal aspects with graph convolutional networks to enhance the regularization for the UGC representation learning, and, hence, fulfil effective fine-grained privacy detection. Extensive experiments on a real-world dataset demonstrate the superiority of the proposed model over state-of-the-art competitors in terms of eight standard metrics. As a byproduct, we have released the codes and involved parameters to facilitate the research community.

A New Private Information Encryption Method in Internet of Things under Cloud Computing Environment

The existence of Internet of Things (IoT) facilitates the collection and transmission of urban data information. However, it can leak users’ personal privacy information in smart cities. Therefore, we propose a new private information encryption method in IoT under cloud computing environment. Under IoT, according to the properties and acquisition time, privacy information can be divided into many subspaces. Then, we analyze the private information encryption with different levels. Based on the stream cipher mechanism, we design an encryption system model of information collection. In the subspace, the privacy information is encrypted and transferred to the relay node. After encrypting, they are segmented and restructured. The long privacy information is divided into smaller slices. Then, they are reintegrated after conversion. Finally, we use stream cipher and dual-key algorithm to complete freedom nondestructive transformation between plaintext and ciphertext to ensure the integrity of the encrypted private information. Experimental results show that the proposed method takes less time in the encryption and decryption process, which has better ciphertext conversion output effect and suffers fewer network attacks in the same encryption time. The message encryption and decryption time is less than that of other methods. In terms of calculation cost, the proposed method decreases by approximately 14%. What is more, it has higher security and improves the security and integrity of the privacy information collection process.

A Solution to Privacy Preservation in Publishing Human Trajectories

With rapid development of ubiquitous computing and location-based services (LBSs), human trajectory data and associated activities are increasingly easily recorded. Inappropriately publishing trajectory data may leak users’ privacy. Therefore, we study publishing trajectory data while preserving privacy, denoted privacy-preserving activity trajectories publishing (PPATP). We propose S-PPATP to solve this problem. S-PPATP comprises three steps: modeling, algorithm design and algorithm adjustment. During modeling, two user models describe users’ behaviors: one based on a Markov chain and the other based on the hidden Markov model. We assume a potential adversary who intends to infer users’ privacy, defined as a set of sensitive information. An adversary model is then proposed to define the adversary’s background knowledge and inference method. Additionally, privacy requirements and a data quality metric are defined for assessment. During algorithm design, we propose two publishing algorithms corresponding to the user models and prove that both algorithms satisfy the privacy requirement. Then, we perform a comparative analysis on utility, efficiency and speedup techniques. Finally, we evaluate our algorithms through experiments on several datasets. The experiment results verify that our proposed algorithms preserve users’ privay. We also test utility and discuss the privacy-utility tradeoff that real-world data publishers may face.

Privacy‐aware PKI model with strong forward security

With the development of network technology, privacy protection and users anonymity become a new research hotspot. The existing blockchain privacy‐aware public key infrastructure (PKI) model can ensure the privacy of users in the authentication process to a certain extent, but there are still problems of the storage and leakage of users' keys. This paper first proposes a strong forward‐secure ring signature scheme based on RSA, which ensures the anonymity of the signing users and the forward‐backward security of the keys. Then, by introducing the ring signature technology into the privacy‐aware PKI model, this paper proposes a privacy‐aware PKI model with strong forward security based on block chains, which not only ensures the users' identity privacy, but also solves the problem of the storage and leakage of the users' keys, greatly improving the success rate and security of the users' identity authentication. Finally, this paper applies the proposed PKI model to anonymous transactions, designs a privacy‐aware anonymous transaction model with strong forward security, realizing anonymous transactions without relying on trusted third parties, and implementing users' privacy protection.

Probe Request Based Device Identification Attack and Defense

Wi-Fi network has an open nature so that it needs to face greater security risks compared to wired network. The MAC address represents the unique identifier of the device, and is easily obtained by an attacker. Therefore MAC address randomization is proposed to protect the privacy of devices in a Wi-Fi network. However, implicit identifiers are used by attackers to identify user’s device, which can cause the leakage of user’s privacy. We propose device identification based on 802.11ac probe request frames. Here, a detailed analysis on the effectiveness of 802.11ac fields is given and a novel device identification method based on deep learning whose average f1-score exceeds 99% is presented. With a purpose of preventing attackers from obtaining relevant information by the device identification method above, we design a novel defense mechanism based on stream cipher. In that case, the original content of probe request frame is hidden by encrypting probe request frames and construction of probe request is reserved to avoid the finding of attackers. This defense mechanism can effectively reduce the performance of the proposed device identification method whose average f1-score is below 30%. In general, our research on attack and defense mechanism can preserve device privacy better.

A group preference-based privacy-preserving POI recommender system

Ubiquitous smartphones with their built-in location services enable people to explore various points-of-interest (POIs) through location-based apps, e.g., Yelp and Foursquare City Guide. With these apps, users can receive personalized recommendations on nearby places, e.g., restaurants and arcades, which not only saves them searching time, but also helps find POIs that are of interest to them. One issue with these apps and almost all existing recommender systems is that they require users to share their preference data with the service providers. This information, if not properly used, can leak users’ privacy. In this paper, we propose a group preference-based POI recommendation scheme which fuses matrix factorization and clustering techniques to provide quality recommendations without sacrificing users’ privacy.

Privacy Risk Analysis and Mitigation of Analytics Libraries in the Android Ecosystem

While much effort has been made to detect and measure the privacy leakage caused by the advertising (ad) libraries integrated in mobile applications, analytics libraries, which are also widely used in mobile apps have not been systematically studied for their privacy risks. Different from ad libraries, the main function of analytics libraries is to collect users’ in-app actions. Hence, by design analytics libraries are more likely to leak users’ private information. In this work, we study what information is collected by the analytics libraries integrated in popular Android apps. We design and implement a framework called “Alde”. Given an app, Alde employs both static analysis and dynamic analysis to detect the users’ in-app actions collected by analytics libraries. We also study what private information can be leaked by the apps that use the same analytics library. Moreover, we analyze apps’ privacy policies to see whether app developers have notified the users that their in-app action data is collected by analytics libraries. Finally, we select eight widely used analytics libraries to study and apply our method to 300 popular apps downloaded from both Chinese app markets and Google play. Our experimental results show that some apps indeed leak users’ personal information through analytics libraries even though their genuine purposes of using analytics services are legal. To mitigate such threats, we have developed an app named “ALManager” that leverages the Xposed framework to manage analytics libraries in other apps.

An efficient aggregation scheme resisting on malicious data mining attacks for smart grid

In the smart grid, efficient power supplies require near-real-time users’ electricity usage metering data, but these data might leak users’ private information, e.g., living habits. To address this problem, a number of privacy-preserving data aggregation schemes have been proposed in the literature. In this paper, we present a new type of attack, called malicious data mining attack, by which the adversary can infer a target user’s electricity usage data. When considering this attack, the majority of existing data aggregation schemes have one of the following two shortcomings. In one aspect, the schemes based on homomorphic encryption can output an accurate aggregation result, but most of them are vulnerable to this attack. In another aspect, the schemes based on differential privacy able to withstand this attack, but the random noises introduced prevent accurate aggregation results from being computed. In this paper, we propose a novel data aggregation scheme that is not only secure against the malicious data mining attack, but also capable of outputting an accurate aggregation result. Detailed security analyses indicate that the proposed scheme satisfies the desirable properties for privacy-preserving data aggregation in the smart grid, and the simulated results demonstrate that our proposed scheme enjoys low computation and communication overhead.

A Context-Aware Framework for Detecting Sensor-Based Threats on Smart Devices

Sensors (e.g., light, gyroscope, and accelerometer) and sensing-enabled applications on a smart device make the applications more user-friendly and efficient. However, the current permission-based sensor management systems of smart devices only focus on certain sensors and any App can get access to other sensors by just accessing the generic sensor Application Programming Interface (API). In this way, attackers can exploit these sensors in numerous ways: they can extract or leak users' sensitive information, transfer malware, or record or steal sensitive information from other nearby devices. In this paper, we propose 6thSense, a context-aware intrusion detection system which enhances the security of smart devices by observing changes in sensor data for different tasks of users and creating a contextual model to distinguish benign and malicious behavior of sensors. 6thSense utilizes three different Machine Learning-based detection mechanisms (i.e., Markov Chain, Naive Bayes, and LMT). We implemented 6thSense on several sensor-rich Android-based smart devices (i.e., smart watch and smartphone) and collected data from typical daily activities of 100 real users. Furthermore, we evaluated the performance of 6thSense against three sensor-based threats: (1) a malicious App that can be triggered via a sensor, (2) a malicious App that can leak information via a sensor, and (3) a malicious App that can steal data using sensors. Our extensive evaluations show that the 6thSense framework is an effective and practical approach to defeat growing sensor-based threats with an accuracy above 96 percent without compromising the normal functionality of the device. Moreover, our framework reveals minimal overhead.

Combinational Randomized Response Mechanism for Unbalanced Multivariate Nominal Attributes

At present, many enterprises provide users with better services by collecting their sensitive information. However, these enterprises will inevitably cause the leakage of users' information, thereby infringing on users' privacy. Local differential privacy resolves this problem by only aggregating randomized values from each user, providing plausible deniability. However, different users might have diverse privacy requirements for different attributes. Moreover, the dimensions of these attributes may be unbalanced. Traditional local differential privacy algorithms usually assign the same privacy budget to all attributes, resulting in undesired frequency estimation. To obtain highly accurate of the results while satisfying local differential privacy, the aggregator needs to implement a reasonable privacy budget allocation scheme. Motivated by this, this paper proposed a novel local differential privacy scheme. The proposed method combines the advantages of BRR and MRR to address the problem of high and low privacy requirements. It employs the Lagrange multiplier algorithm to transform the privacy budget allocation problem between unbalanced attributes into a problem of calculating minima from unconditionally constrained convex functions. The solution to the resulting nonlinear equation is used as the final privacy budget allocation scheme. Simulation experiments show that the novel local differential privacy scheme proposed by this paper can significantly reduce the estimation error under the premise of satisfying the local differential privacy.