62782878
Anonymity-preserving data collection
Protection of privacy has become an important problem in data mining. In particular, individuals have become increasingly unwilling to share their data, frequently resulting in individuals either refusing to share their data or providing incorrect data. In turn, such problems in data collection can affect the success of data mining, which relies on sufficient amounts of accurate data in order to produce meaningful results. Random perturbation and randomized response techniques can provide some level of privacy in data collection, but they have an associated cost in accuracy. Cryptographic privacy-preserving data mining methods provide good privacy and accuracy properties. However, in order to be efficient, those solutions must be tailored to specific mining tasks, thereby losing generality.In this paper, we propose efficient cryptographic techniques for online data collection in which data from a large number of respondents is collected anonymously, without the help of a trusted third party. That is, our solution allows the miner to collect the original data from each respondent, but in such a way that the miner cannot link a respondent's data to the respondent. An advantage of such a solution is that, because it does not change the actual data, its success does not depend on the underlying data mining problem. We provide proofs of the correctness and privacy of our solution, as well as experimental data that demonstrates its efficiency. We also extend our solution to tolerate certain kinds of malicious behavior of the participants.
17126561
Experimental Analysis of Privacy-Preserving Statistics Computation
The recent investigation of privacy-preserving data mining and other kinds of privacy-preserving distributed computation has been motivated by the growing concern about the privacy of individuals when their data is stored, aggregated, and mined for information. Building on the study of selective private function evaluation and the efforts towards practical algorithms for privacy-preserving data mining solutions, we analyze and implement solutions to an important primitive, that of computing statistics of selected data in a remote database in a privacy-preserving manner. We examine solutions in different scenarios ranging from a high speed communications medium, such as a LAN or high-speed Internet connection, to a decelerated communications medium to account for worst-case communication delays such as might be provided in a wireless multihop setting.
17075655
Privacy-enhancing k-anonymization of customer data
In order to protect individuals' privacy, the technique of k-anonymization has been proposed to de-associate sensitive attributes from the corresponding identifiers. In this paper, we provide privacy-enhancing methods for creating k-anonymous tables in a distributed scenario. Specifically, we consider a setting in which there is a set of customers, each of whom has a row of a table, and a miner, who wants to mine the entire table. Our objective is to design protocols that allow the miner to obtain a k-anonymous table representing the customer data, in such a way that does not reveal any extra information that can be used to link sensitive attributes to corresponding identifiers, and without requiring a central authority who has access to all the original data. We give two different formulations of this problem, with provably private solutions. Our solutions enhance the privacy of k-anonymization in the distributed scenario by maintaining end-to-end privacy from the original customer data to the final k-anonymous results.
17249203
Privacy-Preserving Queries on Encrypted Data
Data confidentiality is a major concern in database systems. Encryption is a useful tool for protecting the confidentiality of sensitive data. However, when data is encrypted, performing queries becomes more challenging. In this paper, we study efficient and provably secure methods for queries on encrypted data stored in an outsourced database that may be susceptible to compromise. Specifically, we show that, in our system, even if an intruder breaks into the database and observes some interactions between the database and its users, he only learns very little about the data stored in the database and the queries performed on the data. Our work consists of several components. First, we consider databases in which each attribute has a finite domain and give a basic solution for certain kinds of queries on such databases. Then, we present two enhanced solutions, one with a stronger security guarantee and the other with accelerated queries. In addition to providing proofs of our security guarantees, we provide empirical performance evaluations. Our experiments demonstrate that our solutions are fast on large-sized real data.
6436603
Experimental analysis of a privacy-preserving scalar product protocol
The recent investigation of privacy-preserving data mining has been motivated by the growing concern about the privacy of individuals when their data is stored, aggregated, and mined for information. In an effort towards practical algorithms for privacy-preserving data mining solutions, we analyze and implement solutions to an important primitive: the privacy-preserving scalar product of two vectors held by different parties. Privacypreserving scalar products are an important component of privacy-preserving data mining algorithms, particularly when data is vertically partitioned between two or more parties. We examine a cryptographically secure privacypreserving data mining solution in different computational settings. Our experimental results show that in the absence of special-purpose hardware accelerators or practical optimizations, the computational complexity, rather than the communication complexity, is the performance bottleneck. We also evaluate several practical optimizations to improve the efficiency.
11052169
Privacy-Preserving Classification of Customer Data without Loss of Accuracy
Privacy has become an increasingly important issue in data mining. In this paper, we consider a scenario in which a data miner surveys a large number of customers to learn classiflcation rules on their data, while the sensitive attributes of these customers need to be protected. Solutions have been proposed to address this problem using randomization techniques. Such solutions exhibit a tradeofi of accuracy and privacy: the more each customer’s private information is protected, the less accurate result the miner obtains; conversely, the more accurate the result, the less privacy for the customers. In this paper, we propose a simple cryptographic approach that is e‐cient even in a many-customer setting, provides strong privacy for each customer, and does not lose any accuracy as the cost of privacy. Our key technical contribution is a privacy-preserving method that allows a data miner to compute frequencies of values or tuples of values in the customers’ data, without revealing the privacy-sensitive part of the data. Unlike general-purpose cryptographic protocols, this method requires no interaction between customers, and each customer only needs to send a single ∞ow of communication to the data miner. However, we are still able to ensure that nothing about the sensitive data beyond the desired frequencies is revealed to the data miner. To illustrate the power of our approach, we use our frequency mining computation to obtain a privacypreserving naive Bayes classifler learning algorithm. Initial experimental results demonstrate the practical e‐ciency of our solution. We also suggest some other applications of privacy-preserving frequency mining.
2702750
A new look at multimedia synchronization in distributed environments
A multimedia system requires to handle automatically and uniformly the generation, manipulation, presentation, storage, and communication of independent discrete and continuous media data. The synchronization between various kinds of media data is a key issue in an integrated multimedia system, and has drawn a considerable attention. In this paper we investigate the synchronization problem in distributed multimedia environments and propose a new approach. Based on a globally synchronized clock, we present an isochronous clock-driven protocol for multimedia synchronization in a distributed environment. We show that the isochronous approach is simple, easy to understand, and more importantly, can guarantee the temporal ordering (including causal order and /spl Delta/-causal order). We believe that with the new developments in the improved NTP over Internet and very high accuracy of synchronized clocks over ATM and LANs, the clock-based distributed synchronization would provide a promising, yet simple and intuitive, alternative to the existing approaches.
45395929
An Operational Investigation of the CPS Hierarchy
We explore the hierarchy of control induced by successive transformations into continuation-passing style (CPS) in the presence of "control delimiters" and "composable continuations". Specifically, we investigate the structural operational semantics associated with the CPS hierarchy. 
 
To this end, we characterize an operational notion of continuation semantics. We relate it to the traditional CPS transformation and we use it to account for the control operator shift and the control delimiter reset operationally. We then transcribe the resulting continuation semantics in ML, thus obtaining a native and modular implementation of the entire hierarchy. We illustrate it with several examples, the most significant of which is layered monads.
25613636
Efficient distributed SAT and SAT-based distributed Bounded Model Checking
SAT-based Bounded Model Checking (BMC), though a robust and scalable verification approach, still is computationally intensive, requiring large memory and time. Even with the recent development of improved SAT solvers, the memory limitation of a single server rather than time can become a bottleneck for doing deeper BMC search for large designs. Distributing computing requirements of BMC over a network of workstations can overcome the memory limitation of a single server, albeit at increased communication cost. In this paper, we present (a) a method for distributed SAT over a network of workstations using a Master/Client model where each Client workstation has an exclusive partition of the SAT problem and uses knowledge of partition topology to communicate with other Clients, (b) a method for distributing SAT-based BMC using the distributed SAT. For the sake of scalability, at no point in the BMC computation does a single workstation have all the information. We experimented on a network of heterogeneous workstations interconnected with a standard Ethernet LAN. To illustrate, on an industrial design with ∼13 K FFs and ∼0.5 million gates, the non-distributed BMC on a single workstation (with 4 GB memory) ran out of memory after reaching a depth of 120; on the other hand, our SAT-based distributed BMC over 5 similar workstations was able to go up to 323 steps with a communication overhead of only 30%.
44572255
Approximated Classification in Interactive Facial Image Retrieval
For databases of facial images, where each subject is depicted in only one or a few images, the query precision of interactive retrieval suffers from the problem of extremely small class sizes. A potential way to address this problem is to employ automatic even though imperfect classification on the images according to some high level concepts. In this paper we point out that significant improvement in terms of the occurrence of the first subject hit is feasible only when the classifiers are of sufficient accuracy. In this work Support Vector Machines (SVMs) are incorporated in order to obtain high accuracy for classifying the imbalanced data. We also propose an automatic method to choose the penalty factor of training error and the width parameter of the radial basis function used in training the SVM classifiers. More significant improvement in the speed of retrieval is feasible with small classes than with larger ones. The results of our experiments suggest that the first subject hit can be obtained two to five times faster for semantic classes such as “black persons” or “eyeglass-wearing persons”.
40550352
Partial Relevance in Interactive Facial Image Retrieval
For databases of facial images, where each subject has only a few images, the query precision of interactive retrieval suffers from the problem of extremely small class sizes. A novel method is proposed to relieve this problem by applying partial relevance to the interactive retrieval. This work extends an existing content-based image retrieval system, PicSOM, by relaxing the relevance criterion in the early rounds of the retrieval. Moreover, we apply linear discriminant analysis as a preprocessing step before training the Self-Organizing Maps (SOMs) so that the resulting SOMs have stronger discriminative power. The results of simulated retrieval experiments suggest that for semantic classes such as “black persons” or “bearded persons” the first image which depicts the target subject can be obtained three to six times faster than by retrieval without the partial relevance.