This article investigates a symbiotic ambient backscatter communication (AmBC) system, where for the primary system, a source node T1 transmits information to a destination node T2. Whereas for the backscatter system, by riding on T1’s signal, the backscatter device passively conveys its own information$c(n)$to T1 and T2 via backscattering. For such, the coexistence outage probability (COP) and ergodic capacity (EC) of the AmBC system are characterized for three cases of coexistence constraints, i.e., 1) both T1 and T2 decode$c(n)$(Case I); 2) only T2 decodes$c(n)$(Case II); and 3) only T1 decodes$c(n)$(Case III). It is analytically shown that for sufficiently high transmit signal-to-noise ratio (SNR), the COP obeys the scaling law of$({1}/{\sqrt {P_{s}}})$(with$P_{s}$denoting T1’s transmit power) for Cases I and III, whereas its scaling law is determined by$([{\mathrm {log}(P_{s})}]/{P_{s}})$as well as$({1}/{P_{s}})$for Case II. In addition, it is shown that the restriction condition of decoding$c(n)$at T1 results in a dominating term$({1}/{\sqrt {P_{s}}})$for the COP at high SNR, whereas the restriction condition of decoding$c(n)$at T2 results in an infinitesimal relative to$({1}/{\sqrt {P_{s}}})$. It is also shown that for different cases, the effects of the T1–T2 channel statistics on the COP are significantly different. However, unlike the metric of COP, for the EC, the impacts of decoding constraints of$c(n)$gradually disappear at high SNR and the ECs of the backscatter channels for Cases II and III approach, respectively, toward the counterpart for Case I.

Mining frequent subgraphs from a collection of input graphs is an important task for exploratory data analysis on graph data. However, if the input graphs contain sensitive information, releasing discovered frequent subgraphs may pose considerable threats to individual privacy. In this paper, we study the problem of frequent subgraph mining (FSM) under the rigorous differential privacy model. We present a two-phase differentially private FSM algorithm, which is referred to as DFG. In DFG, frequent subgraphs are privately identified in the first phase, and the noisy support of each identified frequent subgraph is calculated in the second phase. In particular, to privately identity frequent subgraphs, we propose a frequent subgraph identification approach, which can improve the accuracy of discovered frequent subgraphs through candidate pruning. Moreover, to compute the noisy support of each identified frequent subgraph, we devise a lattice-based noisy support computation approach, which leverages the inclusion relations between the discovered frequent subgraphs to improve the accuracy of the noisy supports. Through formal privacy analysis, we prove that DFG satisfies ε-differential privacy. Extensive experimental results on real datasets show that DFG can privately find frequent subgraphs while achieving high data utility.

Frequent itemset mining (FIM) is one of the most fundamental problems in data mining. It has practical importance in a wide range of application areas such as decision support, Web usage mining, bioinformatics, etc. Given a database, where each transaction contains a set of items, FIM tries to find itemsets that occur in transactions more frequently than a given threshold. Despite valuable insights the discovery of frequent itemsets can potentially provide, if the data is sensitive (e.g., web browsing history and medical records), releasing the discovered frequent itemsets might pose considerable threats to individual privacy.

Mining frequent subgraphs from a collection of input graphs is an important topic in data mining research. However, if the input graphs contain sensitive information, releasing frequent subgraphs may pose considerable threats to individual's privacy. In this paper, we study the problem of frequent subgraph mining (FGM) under the rigorous differential privacy model. We introduce a novel differentially private FGM algorithm, which is referred to as DFG. In this algorithm, we first privately identify frequent subgraphs from input graphs, and then compute the noisy support of each identified frequent subgraph. In particular, to privately identify frequent subgraphs, we present a frequent subgraph identification approach which can improve the utility of frequent subgraph identifications through candidates pruning. Moreover, to compute the noisy support of each identified frequent subgraph, we devise a lattice-based noisy support derivation approach, where a series of methods has been proposed to improve the accuracy of the noisy supports. Through formal privacy analysis, we prove that our DFG algorithm satisfies ε-differential privacy. Extensive experimental results on real datasets show that the DFG algorithm can privately find frequent subgraphs with high data utility.

Ground penetrating radar (GPR) using a 3D antenna array is employed for data acquisition over an area contaminated with landmine simulants. The performance of different processing methods applied in 1D, 2D and 3D radiograms are evaluated on real data obtained from two different test sites. Preliminary results are presented on the effectiveness of 3D GPR and on the applicability and limitations of widely used processing schemes.

In this paper, we study the problem of mining frequent sequences under the rigorous differential privacy model. We explore the possibility of designing a differentially private frequent sequence mining (FSM) algorithm which can achieve both high data utility and a high degree of privacy. We found, in differentially private FSM, the amount of required noise is proportionate to the number of candidate sequences. If we could effectively prune those unpromising candidate sequences, the utility and privacy tradeoff can be significantly improved. To this end, by leveraging a sampling-based candidate pruning technique, we proposePFS$^2$, a novel differentially private FSM algorithm. It is the first algorithm that supports the general gap-constrained FSM in the context of differential privacy. The gap constraints in FSM can be used to limit the mining results to a controlled set of frequent sequences. In ourPFS$^2$algorithm, the core is to utilize sample databases to prune the candidate sequences generated based on the downward closure property. In particular, we use the noisy local support of candidate sequences in the sample databases to estimate which candidate sequences are potentially frequent. To improve the accuracy of such private estimations, a gap-aware sequence shrinking method is proposed to enforce the length constraint on the sample databases. Moreover, to calibrate the amount of noise required by differential privacy, a gap-aware sensitivity computation method is proposed to obtain the sensitivity of the local support computations with different gap constraints. Furthermore, to decrease the probability of misestimating frequent sequences as infrequent, a threshold relaxation method is proposed to relax the user-specified threshold for the sample databases. Through formal privacy analysis, we show that ourPFS$^2$algorithm is$\epsilon$-differentially private. Extensive experiments on real datasets illustrate that ourPFS$^2$algorithm can privately find frequent sequences with high accuracy.

In this paper, we study the problem of mining frequent sequences under the rigorous differential privacy model. We explore the possibility of designing a differentially private frequent sequence mining (FSM) algorithm which can achieve both high data utility and a high degree of privacy. We found, in differentially private FSM, the amount of required noise is proportionate to the number of candidate sequences. If we could effectively reduce the number of unpromising candidate sequences, the utility and privacy tradeoff can be significantly improved. To this end, by leveraging a sampling-based candidate pruning technique, we propose a novel differentially private FSM algorithm, which is referred to as PFS2. The core of our algorithm is to utilize sample databases to further prune the candidate sequences generated based on the downward closure property. In particular, we use the noisy local support of candidate sequences in the sample databases to estimate which sequences are potentially frequent. To improve the accuracy of such private estimations, a sequence shrinking method is proposed to enforce the length constraint on the sample databases. Moreover, to decrease the probability of misestimating frequent sequences as infrequent, a threshold relaxation method is proposed to relax the user-specified threshold for the sample databases. Through formal privacy analysis, we show that our PFS2algorithm is ϵ-differentially private. Extensive experiments on real datasets illustrate that our PFS2algorithm can privately find frequent sequences with high accuracy.

Recently, there has been a growing interest in designing differentially private data mining algorithms. Frequent itemset mining (FIM) is one of the most fundamental problems in data mining. In this paper, we explore the possibility of designing a differentially private FIM algorithm which can not only achieve high data utility and a high degree of privacy, but also offer high time efficiency. To this end, we propose a differentially private FIM algorithm based on the FP-growth algorithm, which is referred to as PFP-growth. The PFP-growth algorithm consists of a preprocessing phase and a mining phase. In the preprocessing phase, to improve the utility and privacy tradeoff, a novel smart splitting method is proposed to transform the database. For a given database, the preprocessing phase needs to be performed only once. In the mining phase, to offset the information loss caused by transaction splitting, we devise a run-time estimation method to estimate the actual support of itemsets in the original database. In addition, by leveraging the downward closure property, we put forward a dynamic reduction method to dynamically reduce the amount of noise added to guarantee privacy during the mining process. Through formal privacy analysis, we show that our PFP-growth algorithm is ε-differentially private. Extensive experiments on real datasets illustrate that our PFP-growth algorithm substantially outperforms the state-of-the-art techniques.