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.
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.
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.
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.
361158
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. Interestingly, with the recent development of improved SAT solvers, it is frequently the memory limitation of a single server rather than time that becomes a bottleneck for doing deeper BMC search. 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 worsktation 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 heterogenous workstations interconnected with a standard Ethernet LAN. To illustrate, on an industrial design with 13K FFs and 0.5M gates, the non-disributed BMC on a single workstation (with 4 Gb memory) ran out of memroy after reaching a depth of 120; on the otherhand, our SAT-based distributed BMC over 5 similar workstations was able to go upto 323 steps with a communication overhead of only 30%.
15396672
Maintaining System State Information in a Multiagent Environment for Effective Learning
One fundamental issue in multiagent reinforcement learning is how to deal with the limited local knowledge of an agent in order to achieve effective learning. In this paper, we argue that this issue can be more effectively solved if agents are equipped with a consistent global view. We achieve this by requiring agents to follow an interacting protocol. The properties of the protocol are derived and theoretically analyzed. A distributed protocol that satisfies these properties is presented. The experimental evaluations are conducted for a well-known test-case (i.e., pursuit game) in the context of two learning algorithms. The results show that the protocol is effective and the reinforcement learning algorithms using it perform much better. key words: multiagent system, system state, distributed protocol, token ring
8854168
Execution of A Requirement Model in Software Development
Latest research results have shown that requirements errors have a prolonged impact on software development and that they are more expensive to fix during later stages than early stages in software development. Use case diagrams in UML are used to give requirements for a software system, but all descriptions for each use case are written in informal language. In this paper, we propose a new language HCL (High-Level Constraint Language) to which any requirement model given by use case diagrams can be mapped. Not only is the language HCL based on a formal language but also the requirement model written in HCL can be executed. Many errors occurring during requirements analysis and design can be detected by means of execution.
2501005
Coordinating Multiple Agents via Reinforcement Learning
In this paper, we attempt to use reinforcement learning techniques to solve agent coordination problems in task-oriented environments. The Fuzzy Subjective Task Structure model (FSTS) is presented to model the general agent coordination. We show that an agent coordination problem modeled in FSTS is a Decision-Theoretic Planning (DTP) problem, to which reinforcement learning can be applied. Two learning algorithms, ‘‘coarse-grained’’ and ‘‘fine-grained’’, are proposed to address agents coordination behavior at two different levels. The ‘‘coarse-grained’’ algorithm operates at one level and tackle hard system constraints, and the ‘‘fine-grained’’ at another level and for soft constraints. We argue that it is important to explicitly model and explore coordination-specific (particularly system constraints) information, which underpins the two algorithms and attributes to the effectiveness of the algorithms. The algorithms are formally proved to converge and experimentally shown to be effective.