Skein is our submission (myself and seven others: Niels Ferguson, Stefan Lucks, Doug Whiting, Mihir Bellare, Tadayoshi Kohno, Jon Callas, and Jesse Walker). Here's the paper:

Executive Summary

Skein is a new family of cryptographic hash functions. Its design combines speed, security, simplicity, and a great deal of flexibility in a modular package that is easy to analyze.

Skein is fast. Skein-512 -- our primary proposal -- hashes data at 6.1 clock cycles per byte on a 64-bit CPU. This means that on a 3.1 GHz x64 Core 2 Duo CPU, Skein hashes data at 500 MBytes/second per core -- almost twice as fast as SHA-512 and three times faster than SHA-256. An optional hash-tree mode speeds up parallelizable implementations even more. Skein is fast for short messages, too; Skein-512 hashes short messages in about 1000 clock cycles.

Skein is secure. Its conservative design is based on the Threefish block cipher. Our current best attack on Threefish-512 is on 25 of 72 rounds, for a safety factor of 2.9. For comparison, at a similar stage in the standardization process, the AES encryption algorithm had an attack on 6 of 10 rounds, for a safety factor of only 1.7. Additionally, Skein has a number of provably secure properties, greatly increasing confidence in the algorithm.

Skein is simple. Using only three primitive operations, the Skein compression function can be easily understood and remembered. The rest of the algorithm is a straightforward iteration of this function.

Skein is flexible. Skein is defined for three different internal state sizes -- 256 bits, 512 bits, and 1024 bits -- and any output size. This allows Skein to be a drop-in replacement for the entire SHA family of hash functions. A completely optional and extendable argument system makes Skein an efficient tool to use for a very large number of functions: a PRNG, a stream cipher, a key derivation function, authentication without the overhead of HMAC, and a personalization capability. All these features can be implemented with very low overhead. Together with the Threefish large-block cipher at Skein core, this design provides a full set of symmetric cryptographic primitives suitable for most modern applications.

Skein is efficient on a variety of platforms, both hardware and software. Skein-512 can be implemented in about 200 bytes of state. Small devices, such as 8-bit smart cards, can implement Skein-256 using about 100 bytes of memory. Larger devices can implement the larger versions of Skein to achieve faster speeds.

Skein was designed by a team of highly experienced cryptographic experts from academia and industry, with expertise in cryptography, security analysis, software, chip design, and implementation of real-world cryptographic systems. This breadth of knowledge allowed them to create a balanced design that works well in all environments.

Here's source code, text vectors, and the like for Skein. Watch the Skein website for any updates -- new code, new results, new implementations, the proofs.

NIST's deadline is Friday. It seems as if everyone -- including many amateurs -- is working on a hash function, and I predict that NIST will receive at least 80 submissions. (Compare this to the 21 submissions NIST received -- five were rejected as not being complete -- for the AES competition in 1998.) I expect people to start posting their submissions over the weekend. (Ron Rivest already presented MD6 at Crypto in August.) Probably the best place to watch for new hash functions is here; I'll try to keep a listing of the submissions myself.

The selection process will take around four years. I've previously called this sort of thing a cryptographic demolition derby -- last one left standing wins -- but that's only half true. Certainly all the groups will spend the next couple of years trying to cryptanalyze each other, but in the end there will be a bunch of unbroken algorithms; NIST will select one based on performance and features.

NIST has stated that the goal of this process is not to choose the best standard but to choose a good standard. I think that's smart of them; in this process, "best" is the enemy of "good." My advice is this: immediately sort them based on performance and features. Ask the cryptographic community to focus its attention on the top dozen, rather than spread its attention across all 80 -- although I also expect that most of the amateur submissions will be rejected by NIST for not being "complete and proper." Otherwise, people will break the easy ones and the better ones will go unanalyzed.

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ATC is an excellent working example of complex event processing.   Radar and GPS provide the basic sensory information to accurately track and trace the position of each aircraft in the area of responsibility (AOR) of a particular control tower/zone.     Naturally,  sensory information is preprocessed and formatted in such a way that the data can be processed upstream by multiple real-time applications.

Before we look at complex ATC scenarios, such as “potential collision” or “aircraft off approach vector” we must trace and trace individual objects, aircraft-objects, accurately with very high confidence.    In addition to tracking aircraft-objects, there is a database of information about the aircraft (ideally), such as make, model, age, range, passengers and other properties about the aircraft-object.      In addition, there is a state-model for each aircraft, for example the aircraft might be “on the ground”, “approaching the runway”, “cleared for takeoff”, “cruising altitude”, “approaching runway”, “final decent” etc.  

Tracking and tracing individual aircraft is what is generally referred to as “object refinement” in our CEP/EP reference architecture.   The reason we call this function “object refinement” is that system engineers are focused on optimizing the situational knowledge about individual objects.     Sometimes we refer to this function as “track and trace” because that is what we are doing to  each object in the model.  In Marc Adler’s recent shoplifting scenario, Marc was interested in tracking and tracing people in a store using imaging processing techniques to estimate their behavioral patterns.  In the same way, before we can process for scenarios such as “potential shoplifter” or “suspicious criminal gang activity” we must be able to accurately process (track and trace) individual object, such as people or merchandise.

Back to aircraft and ATC, the “complex event processing” begins when we are looking about object-object relationships, in this model, aircraft-to-aircraft, but this is an overly simplistic model, as we have not yet added (to our model) ground features (towers, buildings, power lines), weather (storm cells, wind) and other flying objects (known migratory bird paths, swarms of insects) to our simple model.  

Complex event processing occurs when we are processing multiple objects in our model looking for threats in real-time.     Practically speaking, all ATC applications are CEP applications.  This means that vendors and integrators who build ATC applications are also CEP vendors.   

Editorial Note: CEP/EP has been around for a long time and was not recently invented in the past decade as some “inventors” would like for us to believe. 

As you can imagine, there is considerable “complex event processing” that goes on “behind the scenes” to provide air traffic controllers and pilots situational knowledge into the “friendly skies”.   As you might further imagine, the situation is more complex when the skies are “not so friendly”, for example, in air combat situations.   

Processing myriad objects is not the end of the processing “chain”.  For example, decisions are being made constantly about potential damage, alternative airports, and more.    In our reference model, we refer to this, generally speaking, as “impact assessment” because we must take an estimated detected complex event, for example “aircraft collision,” and estimate potential damage based on numerous factors such as, the amount of jet fuel in the aircrafts and the location of the aircrafts (over a large city or rural area, near a hospital and emergency services).   Regardless of the scenario, an impact assessment is normally required before optimal decisions can be made.

This is true, by the way, for our shoplifting example (the impact is different if a piece of gum is stolen versus a $1,000,000 diamond necklace or weapons-grade nuclear material) and other scenarios and models.  Static data (information about objects) is required for accurate decision processing.  

Impact assessment is not the end of the “knowledge chain”.    Decisions are constantly being made that effect resources.  For example, suggestion an alternative route for an aircraft is a resource management decision.    Turning on and off radar or switching to alternative tracking devices is a resource management function.  In our CEP/EP reference model (based on the JDL data fusion model), we call this “resource management”.   This function includes contacting emergency services and directing them to a potential crash location or sending out a message to instruct all aircraft to stay off a certain radio frequency.  Resource management is critical.

Our simple ATC model today is by no means complete, it just scratches the surface.  In fact, I have a very close friend, Mark Secrist, who is a former Marine fighter pilot and currently a senior captain for American Airlines.   I have asked Mark to read this post and help me further refine this crude “laymans” ATC model (Thanks Mark!).

Marc’s initial shoplifting model in his post is based on John Colapinto’s concepts of matching a pattern of customer movements in the store with their estimated patterns of shoplifting behavioral patterns.    Marc’s asks how Coral8 might address this.   We are not ready to seek a vendor solution.  We do not yet have a workable detection model.

As indicated above, I don’t think the example situation cited by John and Marc is a viable model for automated processing.    Tracking the behavior of customer’s movements, by machine, would require some very sophisticated image processing technology that would be too expensive compared to any possible loss at most retails stores.    This type of behavioral pattern recognition. in retail stores, is performed by people (security personnel), not machines, observing people.  

To develop a machine pattern recognition application to detect retail shoplifting we need to build detection models that are economically feasible.  If we are going to use a model of shoplifting pattern recognition versus anomaly detection, we need to define the objects we must track.  

In the most simple model, we have merchandise-objects.   Stores normally (physically) track merchandise-objects only at the exit/entry points of the store using some electromagnetic proximity detection technology.   In this model, the detection configuration is a combination of simple alerting with humans watching the store (”minding the store”).    This is not complex event processing.

However, if we added another object to our model, the customer-object, then we start to get more “complex,” but we have not defined “complexity” yet because we have not defined the object properties, the possible states of the objects, and the relationships between the objects that are the basis for estimated situations.

Hence, model building is constrained by available resources, simple economics and risk (cost-benefit).  If we are detecting shoplifting in Walmart the cost-benefit model for implementing an automated shoplifting detection system would be different than at a top diamond store on 5th Avenue in NYC.   Protecting loss at a weapons-grade uranium respository follows a different model than protecting loss at a handicraft shop, naturally.

Like Marc, I find models to automatically detect shoplifting interesting, so permit me to close with a general discussion of shoplifting in the context of our CEP/EP reference model.

One approach would be do determine what objects will be represented in our model.   For example, if we are going to track merchandise, we need to model the ”merchandise-object”.  If we are going to track people, we need to define the properties of this “person object.”  If we are going to represent the store layout, we need to define all these objects (store-object, table-object, shelf-object, entry-object and so forth).  The model can get “complex” quite quickly. 

Editorial Note:  An object-oriented approach greatly assists complex model building because we can benefit from OO properties such as encapsulation and polymorphism.  For example, we can define a basic “person object class” and then create superclasses of this object for “customer-object”, “manager-object”, “or criminal-object.”

Generally speaking, each object we define will require a state-model, for example, in Marc’s example of a customer moving around the store, we would need to model the possible states (customer at the entrance, at table 1, at table 2, at shelf 1, in the bathroom, at the cashier, etc.)  Indeed Marc, this is complex event processing if we have modelled multiple objects and defined object-object relationships that indicate situations of interest.   For example, customer-object at table2 where merchandise-object has the property of  ”very expensive, high risk” and then customer-object changes state to “in bathroom”.  Of course, we need more key indicators, but you get the idea.

Right now, I am typing from the Taste from Heaven Vegetarian Restaurant in Chiang Mai and my battery is running low.  The owner of this excellent restaurant also runs the Elephant Nature Park, a non-profit organization advocating and acting on behalf of the rights of the mighty elephants in Thailand.  Would be great if we could also automatically detect the situation of “elephant abuse” by poachers and other crimes against nature.   Time to get back to my delicious mushroom salad, Northeastern Thai style.

As always, thanks for reading, time for me to get back to eating!

adaptive process management (n.) an element of resource and business process management, adaptive search and event processing. Sometimes referred to as “Level 4” event processing or process refinement.

application concept (n.) a definition of a set of properties that represent the data fields of an application entity. An application concept can describe relationships among themselves. For example, an order concept might have a parent/child relationship with an item concept. A department concept might be related to a purchase requisition concept based on the shared property, department_id. Application concepts can include an application state model.

application state modeler (n.) a UML-compliant application that allows you to model the life cycle of a concept instance — that is, for each instance of a given concept, you can define which states it will pass through and how it will transition from state to state. States have entry actions, exit actions, and conditions, providing precision control over the behavior of an event processing agent. Transitions between states also may have rules. Multiple types of states and transitions maximize the versatility and power of the application state modeler.

derived event (n.) an event that is created as a result of processing one or more other events.

complex event (n.) an event that is a situation-entity abstraction of two or more simple, derived or other complex events.

complex event processing (n.) CEP is a technology for extracting information from message-based systems. CEP is primarily an event processing concept that deals with the task of processing multiple events from an event cloud with the goal of identifying the meaningful events within the event cloud. CEP employs techniques such as detection of complex patterns of many events, event correlation and abstraction, event hierarchies, and relationships between events such as causality, membership, and timing, and event-driven processes.

event (n.) a instance of an event definition. It is an immutable object that represents a business activity that happened at a single point in time. Just as one cannot change the fact that a given activity occurred, one cannot change an event — events are immutable.

event aggregation (n.) the aggregation of simple, derived or complex events into higher levels of event abstractions.

event definition (n.) a set of properties related to a given activity that represents an important or interesting change of state in a human, system or computational activity. An event definition includes event properties such as event priority, event time to live (TTL), and a description of the payload, which is comprehensive information related to the activity that occurred. Events expire when the TTL has elapsed, unless the event processing agent has instructions to consume them prior to that time.

event channel (n.) a communications channel in which events are transmitted from event source to event receivers, typically received as electronic messages. Each channel can have multiple destination and. events can be configured to transmit to a default destination. JMS is an example of an event channel.

event cloud (n.) a partially ordered set of events (poset), either bounded or unbounded, where the partial orderings are imposed by the causal, timing and other relationships between the events. Typically an event cloud is created by the events produced by one or more distributed systems. An event cloud may contain many event types, event streams and event channels. The difference between a cloud and a stream is that there is no event relationship that totally orders the events in a cloud.

event-driven (n.) the behavior of a human, system or computational entity whose execution or actuation is in response to events, typically received as electronic messages.

event-driven architecture (n.) an architectural style for distributed computing applications in which some of the components are event-driven and communicate by means of events.

event processing (n.) computing that performs operations on events, including modifying, creating and destroying events.

event-object (n.) an software object that represents an event, generally for the purpose of computer processing, that exhibits both encapsulation, inheritance and polymorphism.

event prediction (n.) computational activity where the impact of events, complex events, and situations caused by events identified, including both opportunity or threat. Sometimes referred to as “Level 2” event processing, impact assessment or predictive analytics.

event pre-processing (n.) computational activity where events are cleansed or normalized to produce semantically understandable data. Sometimes referred to as “Level 0” event processing.

event processing (n.) computational activities on events dealing with the association, correlation, and combination of event data and information from single and multiple event sources to achieve refined identity and situation estimates for observed event objects, and to achieve complete and timely assessments of opportunities, threats, and their significance. Event processing is characterized by continuous refinements of event estimates and assessments and by evaluation of the need for additional sources, or modification of the process itself, to achieve improved results.

event processing agent (n.) an EPA is a computational entity that performs event processing.

event processing network (n.) a set of event processing agents and a set of event channels connecting them.

event properties (n.) data representation of an event, typically by name-value pairs of type string, integer, real, boolean or a complex data type.

event refinement (n.) filter, identify and track events & make initial processing decisions based on association, correlation and state estimation. Sometimes referred to as “Level 1” event, or event-object, track and trace.

event stream (n.) a time-ordered sequence of events. An event stream may be bounded by a certain time interval or other contextual dimension (content, space, source, certainty), or be open ended and unbounded.

event stream processing (n.) a time-ordered sequence of events. An event stream may be bounded by a certain time interval or other contextual dimension (content, space, source, certainty), or be open ended and unbounded.

rule (n.) defines what triggers unusual, suspicious, problematic, or advantageous activity within an event processing agent and what the EPA does when it discovers these types of activities. Rules execute actions based on certain conditions on events, instances, or a combination of both. A rule includes a group of condition-rule statements and action-rule statements. The condition statements instruct the EPA what to look for in events, and action statements instruct the EPA how to respond when conditions are met. If all the conditions in a rule are satisfied by events or instances or both, the EPA fires the actions. The action might be to execute tasks, create an event instance, modify property values in an event instance, create and send an event, or something else.

rules engine (n.) a type of event processing agent that uses a declarative programming model to process events. Formally described as “an abstract structure that describes a formal language precisely, i.e., a set of rules that mathematically delineates a (usually infinite) set of finite-length strings over a (usually finite) alphabet“. Informally, it can be any system that uses rules, in any form, that can be applied to data to produce outcomes.

rule language (n.) is an artificial language that is used to control the behavior of an event processing agent. Rules languages, like human languages, have syntactic and semantic rules to define meaning.

situation refinement (n.) identify situations, or complex events, based on event clustering, event-event relationships and relationship analysis and context. Sometimes referred to as “Level 2” event processing.

simple event (n.) an event that is not an abstraction or composition of other events.

virtual event (n.) an event that is imagined, modeled or simulated.

ABSTRACT: We examine the security requirements for creating a Deniable File System (DFS), and the efficacy with which the TrueCrypt disk-encryption software meets those requirements. We find that the Windows Vista operating system itself, Microsoft Word, and Google Desktop all compromise the deniability of a TrueCrypt DFS. While staged in the context of TrueCrypt, our research highlights several fundamental challenges to the creation and use of any DFS: even when the file system may be deniable in the pure, mathematical sense, we find that the environment surrounding that file system can undermine its deniability, as well as its contents. Finally, we suggest approaches for overcoming these challenges on modern operating systems like Windows.

There are several threat models against which a DFS could potentially be secure:

• One-Time Access. The attacker has a single snapshot of the disk image. An example would be when the secret police seize Alice’s computer.
• Intermittent Access. The attacker has several snapshots of the disk image, taken at different times. An example would be border guards who make a copy of Alice’s hard drive every time she enters or leaves the country.
• Regular Access. The attacker has many snapshots of the disk image, taken in short intervals. An example would be if the secret police break into Alice’s apartment every day when she is away, and make a copy of the disk each time.

We analyzed the most current version of TrueCrypt available at the writing of the paper, version 5.1a. We shared a draft of our paper with the TrueCrypt development team in May 2008. TrueCrypt version 6.0 was released in July 2008. We have not analyzed version 6.0, but observe that TrueCrypt v6.0 does take new steps to improve TrueCrypt’s deniability properties (e.g., via the creation of deniable operating systems, which we also recommend in Section 5). We suggest that the breadth of our results for TrueCrypt v5.1a highlight the challenges to creating deniable file systems. Given these potential challenges, we encourage the users not to blindly trust the deniability of such systems. Rather, we encourage further research evaluating the deniability of such systems, as well as research on new yet light-weight methods for improving deniability.