Privacy Mechanisms Research Articles

IoT-based Cyber-security of Drones using the Naïve Bayes Algorithm

Recent advancements in drone technology are opening new opportunities and applications: in various fields of life especially in the form of small drones. However, these advancements are also causing new challenges in terms of security, adaptability, and consistency. Small drones are proving to be a new opportunity for the civil and military industries. The small drones are suffering from architectural issues and the definition of security and safety issues. The rapid growth of the Internet of things opens new dimensions for drone technology but posing new threats as well. The tiny flying intelligent devices are challenging for the security and privacy of data. The design of these small drones is yet not matured to fulfill the domain requirements. The basic design issues also need security mechanisms, privacy mechanisms, and data transformations. The aspects like intrusion and interception in the domain of the Internet of Drones (IoD) need to be investigated to make these timely drones more secure and more adaptable. In this paper, we have used intelligent machine learning approach to design an IoT aided drone. This approach will provide intelligent cyber security system which will help in detecting network security threats using Blockchain.

Strongly universally consistent nonparametric regression and classification with privatised data

In this paper we revisit the classical problem of nonparametric regression, but impose local differential privacy constraints. Under such constraints, the raw data (X1,Y1),...,(Xn,Yn), taking values in Rd×R, cannot be directly observed, and all estimators are functions of the randomised output from a suitable privacy mechanism. The statistician is free to choose the form of the privacy mechanism, and here we add Laplace distributed noise to a discretisation of the location of a feature vector Xi and to the value of its response variable Yi. Based on this randomised data, we design a novel estimator of the regression function, which can be viewed as a privatised version of the well-studied partitioning regression estimator. The main result is that the estimator is strongly universally consistent, and we further establish an upper bound on the rate of convergence. Our methods and analysis also give rise to a strongly universally consistent binary classification rule for locally differentially private data.

Privacy in the Cloud: A Survey of Existing Solutions and Research Challenges

Private data is transmitted and stored online every second. Therefore, security and privacy assurances should be provided at all times. However, that is not always the case. Private information is often unwillingly collected, sold, or exposed, depriving data owners of their rightful privacy. In this article, various privacy threats, concepts, regulations, and personal data types are analyzed. An overview of Privacy Enhancing Technologies (PETs) and a survey of anonymization mechanisms, privacy tools, models, and metrics are presented together with an analysis of respective characteristics and capabilities. Moreover, this article analyses the applicability of the reviewed privacy mechanisms on today's Cloud Services and identifies the current research challenges to achieve higher privacy levels in the Cloud.

Privacy-Utility Equilibrium Protocol for Federated Aggregating Multiparty Genome Data

Cloud server aggregates a large amount of genome data from multi genome donors to facilitate scientific research. However, the untrusted cloud server is prone to violate privacy of aggregating genome data. Thus, each genome donor can randomly perturb her genome data using differential privacy mechanism before aggregating. But this is easy to lead to utility disaster of aggregating genome data due to the different privacy preferences of each genome donor, and privacy leakage of aggregating genome data because of the kinship between genome donors. The key challenge here is to achieve an equilibrium between privacy preserving and data utility of aggregating multiparty genome data. To this end, we proposed federated aggregation protocol of multiparty genome data (MGD-FAP) with privacy-utility equilibrium for guaranteeing desired privacy protection and desired data utility. First, we regarded the privacy budget and the accuracy as the desired privacy-utility metrics of genome data respectively. Second, we constructed the federated aggregation model of multiparty genome data by combining random perturbation method of genome data guaranteeing desired data utility with federated comparing update method of local privacy budget achieving desired privacy preserving. Third, we presented the MGD-FAP maintaining privacy-utility equilibrium under the federated aggregation model of multiparty genome data. Finally, our theoretical and experimental analysis showed that MGD-FAP can maintain privacy-utility equilibrium. The MGD-FAP is practical and feasible to ensure the privacy-utility equilibrium of cloud server aggregating multiparty genome data.

Travel Trajectory Frequent Pattern Mining Based on Differential Privacy Protection

Now, many application services based on location data have brought a lot of convenience to people’s daily life. However, publishing location data may divulge individual sensitive information. Because the location records about location data may be discrete in the database, some existing privacy protection schemes are difficult to protect location data in data mining. In this paper, we propose a travel trajectory data record privacy protection scheme (TMDP) based on differential privacy mechanism, which employs the structure of a trajectory graph model on location database and frequent subgraph mining based on weighted graph. Time series is introduced into the location data; the weighted trajectory model is designed to obtain the travel trajectory graph database. We upgrade the mining of location data to the mining of frequent trajectory graphs, which can discover the relationship of location data from the database and protect location data mined. In particular, to improve the identification efficiency of frequent trajectory graphs, we design a weighted trajectory graph support calculation algorithm based on canonical code and subgraph structure. Moreover, to improve the data utility under the premise of protecting user privacy, we propose double processes of adding noises to the subgraph mining process by the Laplace mechanism and selecting final data by the exponential mechanism. Through formal privacy analysis, we prove that our TMDP framework satisfies ε‐differential privacy. Compared with the other schemes, the experiments show that the data availability of the proposed scheme is higher and the privacy protection of the scheme is effective.

Context-Aware Local Information Privacy

In this paper, we study Local Information Privacy (LIP). As a context-aware privacy notion, LIP relaxes the de facto standard privacy notion of local differential privacy (LDP) by incorporating prior knowledge and therefore achieving better utility. We study the relationships between LIP and some of the representative privacy notions including LDP, mutual information and maximal leakage. We show that LIP provides strong instance-wise privacy protection compared to other context-aware privacy notions. Moreover, we present some useful properties of LIP, including post-processing, linkage, composability, transferability and robustness to imperfect prior knowledge. Then we study a general utility-privacy tradeoff framework, under which we derive LIP based privacy-preserving mechanisms for both discrete and continuous-valued data. Three types of perturbation mechanisms are studied in this paper: 1) randomized response (RR), 2) random sampling (RS) and 3) additive noise (AN) (e.g., Gaussian mechanism). Our privacy mechanisms incorporate the prior knowledge into the perturbation parameters so as to enhance utility. Finally, we present a comprehensive set of experiments on real datasets to illustrate the advantage of context-awareness and compare the utility-privacy tradeoffs provided by different mechanisms.

Successive Point-of-Interest Recommendation With Local Differential Privacy

A point-of-interest (POI) recommendation system performs an important role in location-based services because it can help people to explore new locations and promote advertisers to launch advertisements at appropriate locations. The existing POI recommendation systems require raw check-in history of users, which might cause location privacy violations. Although there have been several matrix factorization (MF) based privacy-preserving recommendation systems, they can only focus on user-POI relationships without considering the human movements in check-in history. To tackle this problem, we design a successive POI recommendation framework with local differential privacy, named SPIREL. SPIREL uses two types of information derived from the check-in history as input for the factorization: a transition pattern between two POIs and the visit counts of POIs. We propose a novel objective function for learning the user-POI and POI-POI relationships simultaneously. We further integrate local differential privacy mechanisms in our proposed framework to prevent potential location privacy breaches. Experiments using four public datasets demonstrate that SPIREL achieves better POI recommendation quality while accomplishing stronger privacy protection.

A Differential Privacy Mechanism that Accounts for Network Effects for Crowdsourcing Systems

In crowdsourcing systems, it is important for the crowdsource campaign initiator to incentivize users to share their data to produce results of the desired computational accuracy. This problem becomes especially challenging when users are concerned about the privacy of their data. To overcome this challenge, existing work often aims to provide users with differential privacy guarantees to incentivize privacy-sensitive users to share their data. However, this work neglects the network effect that a user enjoys greater privacy protection when he aligns his participation behaviour with that of other users. To explore this network effect, we formulate the interaction among users regarding their participation decisions as a population game, because a user’s welfare from the interaction depends not only on his own participation decision but also the distribution of others’ decisions. We show that the Nash equilibrium of this game consists of a threshold strategy, where all users whose privacy sensitivity is below a certain threshold will participate and the remaining users will not. We characterize the existence and uniqueness of this equilibrium, which depends on the privacy guarantee, the reward provided by the initiator and the population size. Based on this equilibria analysis, we design the PINE (Privacy Incentivization with Network Effects) mechanism and prove that it maximizes the initiator’s payoff while providing participating users with a guaranteed degree of privacy protection. Numerical simulations, on both real and synthetic data, show that (i) PINE improves the initiator’s expected payoff by up to 75%, compared to state of the art mechanisms that do not consider this effect; (ii) the performance gain by exploiting the network effect is particularly good when the majority of users are flexible over their privacy attitudes and when there are a large number of low quality task performers.

SDN-based security management of multiple WoT Smart Spaces

Next generation infrastructures will be deeply impacted by the introduction of the Internet of Things, with the objective of improving their current operating models and methods. This follows the trend of the Web of Things (WoT), which projects to seamlessly connect various smart devices and IoT frameworks due to its flexible nature, hence, enabling new innovative services and opportunities. However, it also means that more and more data need to be protected from external threats and unauthorized accesses. Therefore, additional security and privacy mechanisms need to be deployed, together with an efficient management of these IoT devices. This issue becomes more convoluted and hardly tractable when dealing with several devices of the infrastructure dispatched over different physical Local Area Networks (LAN)s that we call “Smart Spaces” (SS)s. Thus, this paper proposes to solve this issue by introducing an architecture based on a Software Defined Networking controller for providing a secure access to the different SSs of a smart infrastructure, in a centralized way. We argue that a centralized view can greatly simplify and improve the security management of such infrastructures. Moreover, to support our assumptions, a Proof of Concept, with a concrete implementation of all the components is provided.

Fisher Information Under Local Differential Privacy

We develop data processing inequalities that describe how Fisher information from statistical samples can scale with the privacy parameter ε under local differential privacy constraints. These bounds are valid under general conditions on the distribution of the score of the statistical model, and they elucidate under which conditions the dependence on ε is linear, quadratic, or exponential. We show how these inequalities imply order-optimal lower bounds for private estimation for both the Gaussian location model and discrete distribution estimation for all levels of privacy ε >0. We further apply these inequalities to sparse Bernoulli models and demonstrate privacy mechanisms and estimators with order-matching squared ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> error.

Successive Refinement of Privacy

This work examines a novel question: how much randomness is needed to achieve local differential privacy (LDP)? A motivating scenario is providing multiple levels of privacy to multiple analysts, either for distribution or for heavy hitter estimation, using the same (randomized) output. We call this setting successive refinement of privacy , as it provides hierarchical access to the raw data with different privacy levels. For example, the same randomized output could enable one analyst to reconstruct the input, while another can only estimate the distribution subject to LDP requirements. This extends the classical Shannon (wiretap) security setting to local differential privacy. We provide (order-wise) tight characterizations of privacy-utility-randomness trade-offs in several cases for distribution estimation, including the standard LDP setting under a randomness constraint. We also provide a non-trivial privacy mechanism for multi-level privacy. Furthermore, we show that we cannot reuse random keys over time while preserving privacy of each user.

A differentially private algorithm for range queries on trajectories

We propose a novel algorithm to ensure $$\epsilon $$ -differential privacy for answering range queries on trajectory data. In order to guarantee privacy, differential privacy mechanisms add noise to either data or query, thus introducing errors to queries made and potentially decreasing the utility of information. In contrast to the state of the art, our method achieves significantly lower error as it is the first data- and query-aware approach for such queries. The key challenge for answering range queries on trajectory data privately is to ensure an accurate count. Simply representing a trajectory as a set instead of sequence of points will generally lead to highly inaccurate query answers as it ignores the sequential dependency of location points in trajectories, i.e., will violate the consistency of trajectory data. Furthermore, trajectories are generally unevenly distributed across a city and adding noise uniformly will generally lead to a poor utility. To achieve differential privacy, our algorithm adaptively adds noise to the input data according to the given query set. It first privately partitions the data space into uniform regions and computes the traffic density of each region. The regions and their densities, in addition to the given query set, are then used to estimate the distribution of trajectories over the queried space, which ensures high accuracy for the given query set. We show the accuracy and efficiency of our algorithm using extensive empirical evaluations on real and synthetic data sets.

Privacy preservation auction in a dynamic social network

SummaryThe growing popularity of users in online social network gives a big opportunity for online auction. The famous Information Diffusion Mechanism (IDM) is an excellent methods even meet the incentive compatibility and individual rationality. Although the existing auction in online social network has considered the buyers' information has not known by the seller, current mechanism still cannot preserve the information such as prices. In this paper, we propose a novel mechanism which modeled the auction process in online social network and preserved users' privacy by using differential privacy mechanism. Our mechanism can successfully process the auction and at the same time preserve clients' price information from neighbors. We achieved these by adding Laplace noise for its valuation and the number of valuation seller received in the auction process. We also formulate this mechanism on the real network to show the feasibility and effective of the proposed mechanism.

Research on Selection Method of Privacy Parameter ε

Budget factor is an important factor to measure the intensity of differential privacy, and its allocation scheme has a great impact on privacy protection. This paper studies the selection of the parameter ε in several cases of differential privacy. Firstly, this paper proposes a differential privacy protection parameter configuration method based on fault tolerance interval and analyzes the adversaryʼs fault tolerance under different noise distribution location parameters and scale parameters. Secondly, this paper proposes an algorithm to optimize the application scenarios of multiquery, studies the location parameters and scale parameters in detail, and proposes a differential privacy mechanism to solve the multiuser query scenarios. Thirdly, this paper proposes the differential privacy parameter selection methods based on the single attack and repeated attacks and calculates the upper bound of the parameter ε based on the sensitivity Δ q , the length of the fault tolerance interval L , and the success probability p as long as the fault tolerance interval. Finally, we have carried out a variety of simulation experiments to verify our research scheme and give the corresponding analysis results.

Supporting Privacy, Trust, and Personalization in Online Learning

Privacy is a rather murky concept, but the absence of privacy is clearly felt in today’s digital world. With the significant increase in surveillance power of software and hardware, storage capacity, computing power as well as advancement in data science technologies, the threat to privacy is ever increasing. Within the digital realm, privacy has been studied in different platforms, contexts, and environments. In this capacity, Professor Jim Greer is one of the pioneering thought leaders to study privacy in online learning environment. Additionally, strongly related to privacy, trust and personalization are two important components of online learning. This article reflects on Greer’s contributions to grapple with the following questions: (1) To what extent are privacy, trust, and personalization desired in online learning? (2) How can privacy, trust, and personalization be supported in online learning? In this vein, we study three theories (i.e., limitation theory, control theory, and contextual integrity theory) and different mechanisms (e.g., privacy preferences, identity management, contextual information flow) for privacy. Additionally, this article discusses the ways to achieve trust and personalization without compromising privacy.

Verifiable and Privacy-Preserving Traffic Flow Statistics for Advanced Traffic Management Systems

Crowdsourcing-based traffic monitoring plays an important role in advanced traffic management systems due to its high accuracy and low costs, but it may expose drivers real identities and sensitive locations that results in the privacy leakage of drivers. In this paper, we propose a crowdsourcing-based traffic monitoring scheme that enables a transportation management center (TMC) to achieve traffic flow statistics at road intersections in an efficient, verifiable, and privacy-preserving manner. Specifically, by integrating a homomorphic encryption primitive and a super-increasing sequence, traffic flow can be flexibly structured and encrypted by drivers, i.e., each drivers travel direction at T-junctions or crossroads is protected. As a middle-ware between drivers and TMC, roadside units (RSUs) are introduced to aggregate and further perturb the aggregated encrypted traffic flow based on a differential privacy mechanism. In this way, TMC is capable of acquiring the traffic flow statistics by decrypting the perturbed encrypted traffic flow, without disclosing each individual drivers traffic information. In addition, based on a lightweight commitment proof, the correctness of the encrypted drivers data can be guaranteed, i.e., a selfish driver cannot arbitrarily manipulate his data to poison the aggregated traffic flow. Finally, security analysis demonstrates that the proposed scheme satisfies all desirable security properties, including confidentiality, verifiability, unlinkability, and traceability. Extensive simulations are also conducted to show that the proposed scheme is efficient in terms of low computation and communication costs.

DPCrowd: Privacy-Preserving and Communication-Efficient Decentralized Statistical Estimation for Real-Time Crowdsourced Data

In Internet of Things (IoT) driven smart-world systems, real-time crowd-sourced databases from multiple distributed servers can be aggregated to extract dynamic statistics from a larger population, thus providing more reliable knowledge for our society. Particularly, multiple distributed servers in a decentralized network can realize real-time collaborative statistical estimation by disseminating statistics from their separate databases. Despite no raw data sharing, the real-time statistics could still expose the data privacy of crowd-sourcing participants. For mitigating the privacy concern, while traditional differential privacy (DP) mechanism can be simply implemented to perturb the statistics in each timestamp and independently for each dimension, this may suffer a great utility loss from the real-time and multi-dimensional crowd-sourced data. Also, the real-time broadcasting would bring significant overheads in the whole network. To tackle the issues, we propose a novel privacy-preserving and communication-efficient decentralized statistical estimation algorithm (DPCrowd), which only requires intermittently sharing the DP protected parameters with one-hop neighbors by exploiting the temporal correlations in real-time crowd-sourced data. Then, with further consideration of spatial correlations, we develop an enhanced algorithm, DPCrowd+, to deal with multi-dimensional infinite crowd-data streams. Extensive experiments on several datasets demonstrate that our proposed schemes DPCrowd and DPCrowd+ can significantly outperform existing schemes in providing accurate and consensus estimation with rigorous privacy protection and great communication efficiency.

Arbitrarily Strong Utility-Privacy Tradeoff in Multi-Agent Systems

Each agent in a network makes a local observation that is linearly related to a set of public and private parameters. The agents send their observations to a fusion center to allow it to estimate the public parameters. To prevent leakage of the private parameters, each agent first sanitizes its local observation using a local privacy mechanism before transmitting it to the fusion center. We investigate the utility-privacy tradeoff in terms of the Cramér-Rao lower bounds for estimating the public and private parameters. We study the class of privacy mechanisms given by linear compression and noise perturbation, and derive necessary and sufficient conditions for achieving arbitrarily strong utility-privacy tradeoff in a multi-agent system for both the cases where prior information is available and unavailable, respectively. We also provide a method to find the maximum estimation privacy achievable without compromising the utility and propose an alternating algorithm to optimize the utility-privacy tradeoff in the case where arbitrarily strong utility-privacy tradeoff is not achievable.

Privacy-Preserving Continuous Data Collection for Predictive Maintenance in Vehicular Fog-Cloud

With the advances of Internet of Things (IoT) solutions in intelligent transportation systems, collected vehicle data can produce insights on emerging vehicular phenomenon, and further contribute to the further improvement of innovative and efficient vehicular systems. Particularly, by leveraging data collected from vehicle sensors and maintenance models constructed from operation and repair history, predictive maintenance aims to detect the anomalies of vehicles and provide early warnings before the occurrence of failure. However, privacy preservation still remains as one of the top concerns for vehicle owners in predictive maintenance, as the sensory data could potentially violate their location and identity privacy. To address this challenge, in this article, we propose a privacy-preserving and verifiable continuous data collection scheme with the intent of predictive maintenance in vehicular fog, which gathers and organizes the sensor data of each individual vehicle on a sliding window basis. Specifically, our proposed scheme exploits the homomorphic Paillier cryptosystem and truncated α-geometric technique to protect the content of each individual piece of sensory data. Meanwhile, our proposed scheme also aggregates and authenticates the collected sensory data reports on a time-series sliding window basis, which achieves the continuous observation of the recently collected vehicular sensory data. Detailed security analysis is carried out to demonstrate the security properties of our proposed scheme, including confidentiality, authentication and privacy preservation. In performance evaluations, we also compare our proposed scheme with a traditional scheme, and our scheme shows great improvement in terms of communication and computation overheads. Furthermore, to show the feasibility of our proposed scheme, we also compare and discuss the expected squared error introduced by the differential privacy mechanism.

A Differentially Private Game Theoretic Approach for Deceiving Cyber Adversaries

Cyber deception is one of the key approaches used to mislead attackers by hiding or providing inaccurate system information. There are two main factors limiting the real-world application of existing cyber deception approaches. The first limitation is that the number of systems in a network is assumed to be fixed. However, in the real world, the number of systems may be dynamically changed. The second limitation is that attackers' strategies are simplified in the literature. However, in the real world, attackers may be more powerful than theory suggests. To overcome these two limitations, we propose a novel differentially private game theoretic approach to cyber deception. In this proposed approach, a defender adopts differential privacy mechanisms to strategically change the number of systems and obfuscate the configurations of systems, while an attacker adopts a Bayesian inference approach to infer the real configurations of systems. By using the differential privacy technique, the proposed approach can 1) reduce the impacts on network security resulting from changes in the number of systems and 2) resist attacks regardless of attackers' reasoning power. The experimental results demonstrate the effectiveness of the proposed approach.