With the great amount of available data, especially collected from the ubiquitous Internet of Things (IoT), the issue of privacy leakage has been an increasing concern recently. To preserve the privacy of IoT datasets, traditional methods usually calibrate random noises on the data values to achieve differential privacy (DP) [1]. However, the amount of calibrating noises should be carefully designed and a heedless value will definitely degrade the availability of datasets.

With the great amount of available data, especially collecting from the ubiquitous Internet of Things (IoT), the issue of privacy leakage arises increasingly concerns recently. To preserve the privacy of IoT datasets, traditional methods usually calibrate random noises on the data values to achieve differential privacy (DP). However, the amount of the calibrating noises should be carefully designed and a heedless value will definitely degrade the availability of datasets. Thus, in this work, we propose a stochastic perturbation method to sanitize the dataset, where the perturbation is obtained from the rest samples in the same dataset. In addition, we derive the expression of the utility level based on its unique framework and prove that the proposed algorithm can achieve the$\epsilon$-DP. To show the effectiveness of the proposed algorithm, we conduct extensive experiments on real-life datasets by various functions, such as query answers and machine learning tasks. By comparing with the state-of-the-art methods, our proposed algorithm can achieve a better performance under the same privacy level.

Privacy-preserving estimation of counts of items in streaming data finds applications in several real-world scenarios including word auto-correction and traffic management applications. Recent works of RAPPOR [1] and Apple's count-mean sketch (CMS) algorithm [2] propose privacy preserving mechanisms for count estimation in large volumes of data using probabilistic data structures like counting Bloom filter and CMS. However, these existing methods fall short in providing a sound solution for real-time streaming data applications. Since the size of the data structure in these methods is not adaptive to the volume of the streaming data, the utility (accuracy of the count estimate) can suffer over time due to increased false positive rates. Further, the lookup operation needs to be highly efficient to answer count estimate queries in real-time. More importantly, the local Differential privacy mechanisms used in these approaches to provide privacy guarantees come at a large cost to utility (impacting the accuracy of count estimation). In this work, we propose a novel (local) Differentially private mechanism that provides high utility for the streaming data count estimation problem with similar or even lower privacy budgets while providing: a) fuzzy counting to report counts of related or similar items (for instance to account for typing errors and data variations), and b) improved querying efficiency to reduce the response time for real-time querying of counts. Our algorithm uses a combination of two probabilistic data structures Cuckoo filter and Bloom filter. We provide formal proofs for privacy and utility guarantees and present extensive experimental evaluation of our algorithm using real and synthetic English words datasets for both the exact and fuzzy counting scenarios. Our privacy preserving mechanism substantially outperforms the prior work in terms of lower querying time, significantly higher utility (accuracy of count estimation) under similar or lower privacy guarantees, at the cost of communication overhead.

Textual data, such as clinical notes, product or movie reviews in online stores, transcripts, chat records, and business documents, are widely collected nowadays and can be used to support a large spectrum of Big Data applications. At the same time, textual data, collected about individuals or from individuals, can be susceptible to inference attacks that may leak private and/or sensitive information about individuals.The increasing concerns of privacy risks in textual data preclude sharing or exchanging textual data across different parties/organizations for various applications such as record linkage, similar entity matching, natural language processing (NLP), or machine learning on large collections of textual data. This has led to the development of privacy preserving techniques for applying matching, machine learning or NLP techniques on textual data that contain personal and sensitive information about individuals. While cryptographic techniques are highly secure and accurate, they incur significant amount of computational cost for encoding and matching data – especially textual data – due to the complex nature of text.In this paper, we propose an efficient textual data encoding and matching algorithm using probabilistic techniques based on counting Bloom filters combined with Differential privacy. We apply our algorithm to a popular use case scenario that involves privacy preserving topic modeling – a widely used NLP technique – in order to identify common or collective topics in texts across multiple parties without learning the individual topics of each party, and show its effectiveness in supporting this application. Finally, through extensive experimental evaluation on three large text datasets against a state-of-the-art probabilistic encoding algorithm for privacy preserving LDA topic modelling, we show that our method provides a better privacy-utility trade-off at the cost of more computation complexity and memory space, while still being computationally efficient (log-linear complexity in the size of documents) for Big data compared to cryptographic techniques that have quadratic complexity.

With an increase in low-cost machine learning APIs, advanced machine learning models may be trained on private datasets and monetized by providing them as a service. However, privacy researchers have demonstrated that these models may leak information about records in the training dataset via membership inference attacks. In this paper, we take a closer look at another inference attack reported in literature, called attribute inference, whereby an attacker tries to infer missing attributes of a partially known record used in the training dataset by accessing the machine learning model as an API. We show that even if a classification model succumbs to membership inference attacks, it is unlikely to be susceptible to attribute inference attacks. We demonstrate that this is because membership inference attacks fail to distinguish a member from a nearby non-member. We call the ability of an attacker to distinguish the two (similar) vectors as strong membership inference. We show that membership inference attacks cannot infer membership in this strong setting, and hence inferring attributes is infeasible. However, under a relaxed notion of attribute inference, called approximate attribute inference, we show that it is possible to infer attributes close to the true attributes. We verify our results on three publicly available datasets, five membership, and three attribute inference attacks reported in literature.

In-network aggregation is a technique employed in Wireless Sensor Networks (WSNs) to aggregate information flowing from the sensor nodes towards the base station. It helps in reducing the communication overhead on the nodes in the network and thereby increasing the longevity of the network. We study the problem of maintaing integrity of the aggregate value, when the aggregate function is SUM, in the presence of compromised sensor nodes. We focus on one-round, end-to end, secure aggregation protocols and give a strong, formal security defintion. We show that a worst-case lower bound of Ω(n) applies on the congestion (maximum size of message between any two nodes) in such protocols, where n is the number of nodes in the network. This is the first such result showing that the most basic protocols are the best one-round in-network aggregation protocols with respect to congestion. We also show that against a weaker adversary (which does not compromise nodes), we can achieve secure in-network aggregation protocols with a congestion of O(log2n).

Discovering frequent patterns from data is a popular exploratory technique in datamining. However, if the data are sensitive (e.g., patient health records, user behavior records) releasing information about significant patterns or trends carries significant risk to privacy. This paper shows how one can accurately discover and release the most significant patterns along with their frequencies in a data set containing sensitive information, while providing rigorous guarantees of privacy for the individuals whose information is stored there.

Ad hoc networks are dynamic networks formed "on the fly" by a set of nodes. Achieving secure routing in such networks is a big challenge. Asymmetric signature schemes provide mechanisms for authentication, but may result in inefficient implementations, specially when a large number of nodes is expected. Some of these efficiency problems can be mitigated with the use of aggregate signatures, which reduce the space and computations required for managing many different signatures. In this work we formalize a new concept, aggregate designated verifier signature schemes, which is suitable for authentication of routes in reactive protocols. We propose a specific and efficient scheme with provable security in the random oracle model

A group key agreement (GKA) protocol is a mechanism to establish a cryptographic key for a group of participants, based on each one's contribution, over a public network. The key, thus derived, can be used to establish a secure channel between the participants. When the group composition changes (or otherwise), one can employ supplementary GKA protocols to derive a new key. Thus, they are well-suited to the key establishment needs of dynamic peer-to-peer networks as in ad hoc networks. While many of the proposed GKA protocols are too expensive to be employed by the constrained devices often present in ad hoc networks, others lack a formal security analysis. We present a simple, secure and efficient GKA protocol well suited to dynamic ad hoc networks. We also present results of our implementation of the protocol in a prototype application.

No abstract available.