We present CollectCast, a peer-to-peer (P2P) service for media streaming where a receiver peer is served by multiple sender peers. CollectCast operates at the application level but infers underlying network properties to correlate end-to-end connections between peers. The salient features of CollectCast include: (1) a novel multisender selection method that exploits the performance correlation and dependency among connections between different candidate senders and the receiver, (2) a customization of network tomography techniques and demonstration of improved practicality and efficiency, and (3) an aggregation-based P2P streaming mechanism that sustains receiver-side quality in the presence of sender/network dynamics and degradation. We have performed both real-world (on PlanetLab) and simulation evaluation of CollectCast. Our simulation results show that for a receiver, CollectCast makes better selection of multiple senders than other methods that do not infer underlying network properties. Our PlanetLab experiments are performed using a P2P media streaming application (called PROMISE) which we developed on top of CollectCast. Both packet-level and frame-level performance of MPEG-4 video streaming demonstrates the practicality and effectiveness of CollectCast.

Increasing popularity of online auctions and the associated frauds have drawn the attention of many researchers. It is found that most of the auction sites prefer English auction to other auction mechanisms. The ease of adopting multiple fake identities over the Internet nourishes shill bidding by fraudulent sellers in English auction. In this paper we derive an equilibrium bidding strategy to counteract shill bidding in online English auction. We develop an algorithm based on this strategy.  An eBay like auction environment is simulated. Experiments are conducted in this environment to evaluate this strategy.  Five more popular bidding strategies are compared with the proposed strategy. In the experiment, the bidders are randomly assigned a bidding strategy. All the bidders draw their valuation from the uniform distribution. The bidders compete to buy a product in the presence of a shill. The average expected utility of the agents with proposed strategy is found to be the highest when the auction continues for a longer duration.

We propose a new architecture for on-demand media streaming centered around the peer-to-peer (P2P) paradigm. The key idea of the architecture is that peers share some of their resources with the system. As peers contribute resources to the system, the overall system capacity increases and more clients can be served. The proposed architecture employs several novel techniques to: (1) use the often-underutilized peers

Multimedia data security is important for multimedia commerce. Previous cryptography studies have focused on text data. The encryption algorithms developed to secure text data may not be suitable to multimedia applications because of large data sizes and real time constraint. For multimedia applications, light weight encryption algorithms are attractive. In this paper, we present four fast MPEG video encryption algorithms. These algorithms use a secret key randomly changing the sign bits of of DCT coefficients and/or the sign bits of motion vectors. The encryption effects are achieved by the IDCT during MPEG video decompression processing. These algorithms add very small overhead to MPEG codec. Software implementations are fast enough to meet the real time requirement of MPEG video applications. Our experimental results show that these algorithms can achieve satisfactory results. We believe that they can be used to secure video-on-demand, video conferencing and video email applications.

A mobile ad hoc network is a collection of wireless terminals that can be deployed rapidly. Its deficiencies include limited wireless bandwidth efficiency, low throughput, large delays, and weak security. Integrating it with a wellestablished cellular network can improve communication and security in ad hoc networks, as well as enrich the cellular services. This research proposes a cellular-aided mobile ad hoc network (CAMA) architecture, in which a CAMA agent in the cellular network manages the control information, while the data is delivered through the mobile terminals (MTs). The routing and security information is exchanged between MTs and the agent through cellular radio channels. A position-based routing protocol, the multi-selection greedy positioning routing (MSGPR) protocol, is proposed. At times due to the complicated radio environment, the position information is not precise. Even in these cases, the MT can still find its reachable neighbors (the association) by exchanging ”hello” messages. This association is used in complement with the position information to make more accurate routing decisions. Simulation results show that the delivery ratio in the ad hoc network is greatly improved with very low cellular overhead. The security issues in the proposed architecture and the corresponding solutions are addressed. The experimental study shows that CAMA is much less vulnerable than a pure ad hoc network.

This article recommends the teaching journal as a method of instructional improvement. Drawing on teacher education literature, the article reviews the concept of reflective teaching and then describes uses of the teaching journal for college instructors in descriptive, comparative, and critical dimensions. Teaching journals can improve the teaching not only of beginners but also of experienced instructors.

The antidote to perceived risk is trust, and transactions on the Internet are rife with perceived risk. This article establishes a need for trust messages online in a broader context of declining social trust, reviews trust literature, and then provides four tenets of trust that provide a basis for such rhetorically constructed messages. In addition to offering foundations for the rhetorical construction of trust online, the article presents 2 rhetorical paradoxes of trust that contain both opportunity and danger for scholars and netizens alike.

A lot of people are intimidated by auctions. They fear being recognized for bids they never intended to make; they are confused by the array of items; they are unclear about terms like choice or by the piece, two times the money; 1 and they have difficulty making their minds up fast enough to get in on a sale before the bidding stops. They are afraid of getting stuck with an inferior item at a ridiculously high price. Ultimately, however, they put their trust in the person in charge: the auctioneer.

The auctioneer controls the sometimes frenzied proceedings orally. Cassady (1967, 165) notes, “The auctioneer’s appearance, voice, rhythm of patter, good nature, imperturbability, and storytelling ability may have an effect on bidding activity, thus enhancing prices.” Often amplified by a microphone, the auctioneer’s voice rises above the din of the crowd and assistants to maintain order and demand the attention of prospective bidders. If, for instance, a person is erroneously recognized for a bid, the auctioneer has the discretion and power to make things right. And, naturally, the auctioneer wants to do so, because auctioneers want people to feel comfortable and safe at auctions. Whatever the situation, the auctioneer’s voice organizes and controls the proceedings.

An audible voice is missing, however, from a new kind of auction that has appeared in the past six years. This auction still sells items to the highest bidder, it still can be confusing, and it still is a jumble of items and action and unfamiliar terms, but with one notable absence: there is no auctioneer. This auction is the on-line auction, and instead of a single person orally controlling the auction, there is only a Web site. Instead of a bidder able to observe competing bidders in the crowd, there are only the “buyer” and “seller” usernames. And yet these on-line auctions, led by industry behemoth eBay, have thriven. How is this so? Across cultures and times, auctions [End Page 286] have taken place under the supervision of an auctioneer whose voice commands attention and maintains order. On-line auctions have to create hypertext messages that somehow compensate for the missing orality. This essay argues that eBay and other auction Web sites are actually not that different from live English auctions of livestock, antiques, fish, tobacco, broadcast licenses, household goods, or myriad other items (Cassady 1967). On the contrary, in a virtual space eBay has maintained order and interest by mimicking the auctioneer’s oral style and the rules of in-person English auctions.

k-anonymity provides a measure of privacy protection by preventing re-identification of data to fewer than a group of k data items. While algorithms exist for producing k-anonymous data, the model has been that of a single source wanting to publish data. This paper presents a k-anonymity protocol when the data is vertically partitioned between sites. A key contribution is a proof that the protocol preserves k-anonymity between the sites: While one site may have individually identifiable data, it learns nothing that violates k-anonymity with respect to the data at the other site. This is a fundamentally different distributed privacy definition than that of Secure Multiparty Computation, and it provides a better match with both ethical and legal views of privacy.

n this paper we discuss the need for real-time data mining for many applications in government and industry and describe resulting research issues. We also discuss dependability issues including incorporating security, integrity, timeliness and fault tolerance into data mining. Several different data mining outcomes are described with regard to their implementation in a real-time environment. These outcomes include clustering, association-rule mining, link analysis and anomaly detection. The paper describes how they would be used together in various parallel-processing architectures. Stream mining is discussed with respect to the challenges of performing data mining on stream data from sensors. The paper concludes with a summary and discussion of directions in this emerging area.

ransportation and logistics are a major sector of the economy, however data analysis in this domain has remained largely in the province of optimization. The potential of data mining and knowledge discovery techniques is largely untapped. Transportation networks are naturally represented as graphs. This paper explores the problems in mining of transportation network graphs: we hope to find how current techniques both succeed and fail on this problem, and from the failures, we hope to present new challenges for data mining. Experimental results from applying both existing graph mining and conventional data mining techniques to real transportation network data are provided, including new approaches to making these techniques applicable to the problems. Reasons why these techniques are not appropriate are discussed. We also suggest several challenging problems to precipitate research and galvanize future work in this area.

The ability of databases to organize and share data often raises privacy concerns. Data warehousing combined with data mining, bringing data from multiple sources under a single authority, increases the risk of privacy violations. Privacy preserving data mining provides a means of addressing this issue, particularly if data mining is done in a way that doesn’t disclose information beyond the result. This paper presents a method for privately computing k–nn classification from distributed sources without revealing any information about the sources or their data, other than that revealed by the final classification result.

The growth in interchange of business and other sensitive data has led to increasing interest in access control. While broad-based access control may be adequate for library-style document bases, new applications demand different access rights on different documents, or different parts of a document. Methods have been developed that enforce fine-grained access control in XML, but the administrative complexity of hard-coding rules is still a challenge. We present an XQuery-based approach for deriving access control rules from schemalevel rules, document or database content, or rules on other documents. This approach provides a novel capability to exploit non-structural information in broadly-applicable rules, making it feasible to specify data- and context-dependent rules for large document sets.

Privacy and security concerns can prevent sharing of data, derailing data mining projects. Distributed knowledge discovery, if done correctly, can alleviate this problem. The key is to obtain valid results, while providing guarantees on the (non)disclosure of data. We present a method for k-means clustering when different sites contain different attributes for a common set of entities. Each site learns the cluster of each entity, but learns nothing about the attributes at other sites.