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.
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.
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.
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.
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.
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.
1556378
The second Futamura projection for type-directed partial evaluation
The second Futamura projection describes the automatic generation of non-trivial generating extensions by applying a partial evaluator to itself. We derive an ML implementation of the second Futamura projection for Type-Directed Partial Evaluation (TDPE). Due to the differences between `traditional', syntax-directed partial evaluation and TDPE, this derivation involves several conceptual and technical steps. These include a suitable formulation of the second Futamura projection and techniques for using TDPE to specialize type-indexed programs. In the context of the second Futamura projection, we also compare and relate TDPE with conventional offline partial evaluation.
We demonstrate our technique with several examples, including compiler generation for Tiny, a prototypical imperative language.
14662988
Maximum Likelihood Analysis of Adaptive Evolution in HIV-1 Gp120 env Gene
Every functional protein appears to have some conserved amino acids which are critically important to the basic structure and function of the protein and thus under purifying selection. Some proteins also have variable amino acids which, when changed, offer a selective advantage, and thus undergo adaptive evolution. These amino acids are also important to the structure and function of the protein, although in a different way. It seems that the selective pressure in every protein varies among sites. Maximum likelihood models developed recently for comparison of silent and replacement nucleotide substitution rates allow for variable selective pressures among amino acid sites, and provide a powerful approach to studying the evolutionary process of protein-coding genes. This paper applies the likelihood models to analyze a data set of 186 HIV-1 gp120 env gene sequences for comparison with a previous analysis of the same data set. The maximum likelihood analysis identified a number of sites under positive selection, some in the conserved regions of the protein.
193663
SAT-Based Image Computation with Application in Reachability Analysis
Image computation nds wide application in VLSI CAD, such as state reachability analysis in formal veri cation and synthesis, combinational veri cation, combinational and sequential test. Existing BDD-based symbolic algorithms for image computation are limited by memory resources in practice, while SAT-based algorithms that can obtain the image by enumerating satisfying assignments to a CNF representation of the Boolean relation are potentially limited by time resources. We propose new algorithms that combine BDDs and SAT in order to exploit their complementary bene ts, and to o er a mechanism for trading o space vs. time. In particular, (1) our integrated algorithm uses BDDs to represent the input and image sets, and a CNF formula to represent the Boolean relation, (2) a fundamental enhancement called BDD Bounding is used whereby the SAT solver uses the BDDs for the input set and the dynamically changing image set to prune the search space of all solutions, (3) BDDs are used to compute all solutions below intermediate points in the SAT decision tree, (4) a ne-grained variable quanti cation schedule is used for each BDD subproblem, based on the CNF representation of the Boolean relation. These enhancements coupled with more engineering heuristics lead to an overall algorithm that can potentially handle larger problems. This is supported by our preliminary results on exact reachability analysis of ISCAS benchmark circuits.
11859697
Building a Semantic-Rich Service-Oriented Manufacturing Environment
Service-orientation has emerged as a new promising paradigm for enterprise integration in the manufacturing sector. In this paper, we focus on the approach and technologies for constructing a service-oriented manufacturing environment. The service orientation is achieved via virtualization in which every thing, including machines, equipments, devices, various data sources, applications, and processes, are virtualized as standard-based Web services. The virtualization approach is based on the emerging Web Services Resource Framework (WS-RF). A case study of virtualizing an AGV system using WS-RF is described. The use of Semantic Web Services technologies to enhance manufacturing Web services for a semantic-rich environment is discussed, focusing on OWL-S for semantic markup of manufacturing Web services and OWL for the development of ontologies in the manufacturing domain. An enterprise integration architecture enabled by Semantic Web service composition is also discussed.