16974710
Trust Negotiation with Hidden Credentials, Hidden Policies, and Policy Cycles
In an open environment such as the Internet, the decision to collaborate with a stranger (e.g., by granting access to a resource) is often based on the characteristics (rather than the identity) of the requester, via digital credentials: Access is granted if Alice’s credentials satisfy Bob’s access policy. The literature contains many examples where protecting the credentials and the access control policies is useful, and there are numerous protocols that achieve this. In many of these schemes, the server does not learn whether the client obtained access (e.g., to a message, or a service via an eticket). A consequence of this property is that the client can use all of her credentials without fear of “probing” attacks by the server, because the server cannot glean information about which credentials the client has (when this property is lacking, the literature uses a framework where the very use of a credential is subject to a policy specific to that credential). The main result of this paper is a protocol for negotiating trust between Alice and Bob without revealing either credentials or policies, when each credential has its own access policy associated with it (e.g., “a top-secret clearance credential can only be used when the other party is a government employee and has a top-secret clearance”). Our protocol carries out this privacy-preserving trust negotiation between Alice and Bob, while enforcing each credential’s policy (thereby protecting sensitive credentials). Note that there can be a deep nesting of dependencies between credential policies, and that there can be (possibly overlapping) policy cycles of these dependencies. Our result is not achieved through the routine use of standard techniques to implement, in this framework, one of the known strategies for trust negotiations (such as the “eager strategy”). Rather, this paper uses novel techniques to implement a non-standard trust negotiation strategy specifically suited to this framework (and in fact unusable outside of this framework, as will become clear). Our work is therefore Portions of this work were supported by Grants IIS-0325345, IIS0219560, IIS-0312357, and IIS-0242421 from the National Science Foundation, Contract N00014-02-1-0364 from the Office of Naval Research, by sponsors of the Center for Education and Research in Information Assurance and Security, and by Purdue Discovery Park’s e-enterprise Center. a substantial extension of the state-of-the-art in privacypreserving trust negotiations.
62613535
OACerts: Oblivious Attribute Certificates
We propose oblivious attribute certificates (OACerts), an attribute certificate scheme in which a certificate holder can select which attributes to use and how to use them. In particular, a user can use attribute values stored in an OACert obliviously, i.e., the user obtains a service if and only if the attribute values satisfy the policy of the service provider, yet the service provider learns nothing about these attribute values. This way, the service provider's access control policy is enforced in an oblivious fashion. To enable the oblivious access control using OACerts, we propose a new cryptographic primitive called oblivious commitment-based envelope (OCBE). In an OCBE scheme, Bob has an attribute value committed to Alice and Alice runs a protocol with Bob to send an envelope (encrypted message) to Bob such that: 1) Bob can open the envelope if and only if his committed attribute value satisfies a predicate chosen by Alice and 2) Alice learns nothing about Bob's attribute value. We develop provably secure and efficient OCBE protocols for the Pedersen commitment scheme and comparison predicates as well as logical combinations of them
7496680
Automated trust negotiation using cryptographic credentials
In automated trust negotiation (ATN), two parties exchange digitally signed credentials that contain attribute information to establish trust and make access control decisions. Because the information in question is often sensitive, credentials are protected according to access control policies. In traditional ATN, credentials are transmitted either in their entirety or not at all. This approach can at times fail unnecessarily, either because a cyclic dependency makes neither negotiator willing to reveal her credential before her opponent, because the opponent must be authorized for all attributes packaged together in a credential to receive any of them, or because it is necessary to fully disclose exact attribute values, rather than merely proving they satisfy some predicate (such as being over 21 years of age). Recently, several cryptographic credential schemes and associated protocols have been developed to address these and other problems. However, they can be used only as fragments of an ATN process. This paper introduces a framework for ATN in which the diverse credential schemes and protocols can be combined, integrated, and used as needed. A policy language is introduced that enables negotiators to specify authorization requirements that must be met by an opponent to receive various amounts of information about certified attributes and the credentials that contain it. The language also supports the use of uncertified attributes, allowing them to be required as part of policy satisfaction, and to place their (automatic) disclosure under policy control.
9481657
Private collaborative forecasting and benchmarking
Suppose a number of hospitals in a geographic area want to learn how their own heart-surgery unit is doing compared with the others in terms of mortality rates, subsequent complications, or any other quality metric. Similarly, a number of small businesses might want to use their recent point-of-sales data to cooperatively forecast future demand and thus make more informed decisions about inventory, capacity, employment, etc. These are simple examples of cooperative benchmarking and (respectively) forecasting that would benefit all participants as well as the public at large, as they would make it possible for participants to avail themselves of more precise and reliable data collected from many sources, to assess their own local performance in comparison to global trends, and to avoid many of the inefficiencies that currently arise because of having less information available for their decision-making. And yet, in spite of all these advantages, cooperative benchmarking and forecasting typically do not take place, because of the participants' unwillingness to share their information with others. Their reluctance to share is quite rational, and is due to fears of embarrassment, lawsuits, weakening their negotiating position (e.g., in case of over-capacity), revealing corporate performance and strategies, etc. The development and deployment of <i>private</i> benchmarking and forecasting technologies would allow such collaborations to take place without revealing any participant's data to the others, reaping the benefits of collaboration while avoiding the drawbacks. Moreover, this kind of technology would empower smaller organizations who could then cooperatively base their decisions on a much broader information base, in a way that is today restricted to only the largest corporations. This paper is a step towards this goal, as it gives protocols for forecasting and benchmarking that reveal to the participants the desired answers yet do not reveal to any participant any other participant's private data. We consider several forecasting methods, including linear regression and time series techniques such as moving average and exponential smoothing. One of the novel parts of this work, that further distinguishes it from previous work in secure multi-party computation, is that it involves floating point arithmetic, in particular it provides protocols to securely and efficiently perform division.
14720139
Evaluation strategies for automatic linguistic indexing of pictures
With the rapid technological advances in machine learning and data mining, it is now possible to train computers with hundreds of semantic concepts for the purpose of annotating images automatically using keywords and textual descriptions. We have developed a system, the automatic linguistic indexing of pictures (ALIP) system, using a 2-D multiresolution hidden Markov model. The evaluation of such approaches opens up challenges and interesting research questions. The goals of linguistic indexing are often different from those of other fields including image retrieval, image classification, and computer vision. In many application domains, computer programs that can provide semantically relevant keyword annotations are desired, even if the predicted annotations are different from those of the gold standard. In this paper, we discuss evaluation strategies for automatic linguistic indexing of pictures. We provide both objective and subjective evaluation methods. Finally, we report experimental results using our ALIP system.
195883433
Genescene: biomedical text and data mining
To access the content of digital texts efficiently, it is necessary to provide more sophisticated access than keyword based searching. Genescene provides biomedical researchers with research findings and background relations automatically extracted from text and experimental data. These provide a more detailed overview of the information available. The extracted relations were evaluated by qualified researchers and are precise. A qualitative ongoing evaluation of the current online interface indicates that this method to search the literature is more useful and efficient than keyword based searching.
14405607
mSSL: extending SSL to support data sharing among collaborative clients
Client-server applications often do not scale well when a large number of clients access a single server. To solve this, a new trend is to allow a client to download data from other peer clients, in addition to from the server directly. This paradigm, which we call the hybrid peer-to-peer paradigm, is friendly to the server's scalability, but also faces new security challenges. For example, how can the server authenticate its clients and support data confidentiality? How can a client trust the data downloaded from other clients? What if a client refuses to acknowledge the service it received or overstates the service it offered? In this paper, we present a protocol, called mSSL, that provides a set of security functions to enable secure sharing of the data of a server among its clients. In addition to access control and confidentiality support, mSSL provides an original design on supporting data integrity and proof of service in this new context. Our evaluation further shows that mSSL has a reasonable overhead
14732835
Using fixed point theorems to model the binding in protein-protein interactions
The binding in protein-protein interactions exhibits a kind of biochemical stability in cells. The mathematical notion of fixed points also describes stability. A point is a fixed point if it remains unchanged after a transformation by a function. Many points may not be a fixed point, but they may approach a stable status after multiple steps of transformation. In this paper, we define a point as a protein motif pair consisting of two traditional protein motifs. We propose a function and propose a method to discover stable motif pairs of this function from a large protein interaction, sequence data set. There are many interesting properties for this function (for example, the convergence). Some of them are useful for gaining much efficiency in the discovery of those stable motif pairs; some are useful for explaining why our proposed fixed point theorems are a good way to model the binding of protein interactions. Our results are also compared to biological results to elaborate the effectiveness, of our method.
7021137
Machine annotation and retrieval for digital imagery of historical materials
Annotating digital imagery of historical materials for the purpose of computer-based retrieval is a labor-intensive task for many historians and digital collection managers. We have explored the possibilities of automated annotation and retrieval of images from collections of art and cultural images. In this paper, we introduce the application of the ALIP (Automatic Linguistic Indexing of Pictures) system, developed at Penn State, to the problem of machine-assisted annotation of images of historical materials. The ALIP system learns the expertise of a human annotator on the basis of a small collection of annotated representative images. The learned knowledge about the domain-specific concepts is stored as a dictionary of statistical models in a computer-based knowledge base. When an un-annotated image is presented to ALIP, the system computes the statistical likelihood of the image resembling each of the learned statistical models and the best concept is selected to annotate the image. Experimental results, obtained using the Emperor image collection of the Chinese Memory Net project, are reported and discussed. The system has been trained using subsets of images and metadata from the Emperor collection. Finally, we introduce an integration of wavelet-based annotation and wavelet-based progressive displaying of very high resolution copyright-protected images.