Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Usually, a proof of a theorem contains more knowledge than the mere fact that the theorem is true. For instance, to prove that a graph is Hamiltonian it suffices to exhibit a Hamiltonian tour in it; however, this seems to contain more knowledge than the single bit Hamiltonian/non-Hamiltonian.In this paper a computational complexity theory of the “knowledge” contained in a proof is developed. Zero-knowledge proofs are defined as those proofs that convey no additional knowledge other than the correctness of the proposition in question. Examples of zero-knowledge proof systems are given for the languages of quadratic residuosity and 'quadratic nonresiduosity. These are the first examples of zero-knowledge proofs for languages not known to be efficiently recognizable.

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The knowledge complexity of interactive proof-systems

Cryptography and Coding Theory are closely knitted in many respects Recently, the problem of Decoding Reed Solomon Codes (aka Polynomial Reconstruction) was suggested as an intractability assumption upon which the security of cryptographic protocols can be based This has initiated a line of research that exploited the rich algebraic structure of the problem and related subproblems of which in the cryptographic setting Here we give a short overview of recent works on the subject and the novel applications that were enabled due to this development

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Polynomial reconstruction based cryptography

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Advances in Cryptology - EUROCRYPT 2004

In recent years, IP protection of FPGA hardware designs has become a requirement for many IP vendors. In [34], Simpson and Schaumont proposed a fundamentally different approach to IP protection on FPGAs based on the use of Physical Unclonable Functions (PUFs). Their work only assumes the existence of a PUF on the FPGAs without actually proposing a PUF construction. In this paper, we propose new protocols for the IP protection problem on FPGAs and provide the first construction of a PUF intrinsic to current FPGAs based on SRAM memory randomness present on current FPGAs. We analyze SRAM-based PUF statistical properties and investigate the trade offs that can be made when implementing a fuzzy extractor.

/pdf/fpga-intrinsic-pufs-and-their-use-for-ip-protection-4zcbpcd9em.pdf

FPGA Intrinsic PUFs and Their Use for IP Protection

We present a new generic black-box traitor tracing model in which the pirate-decoder employs a self-protection technique. This mechanism is simple, easy to implement in any (software or hardware) device and is a natural way by which a pirate (an adversary) which is black-box accessible, may try to evade detection. We present a necessary combinatorial condition for black-box traitor tracing of self-protecting devices. We constructively prove that any system that fails this condition, is incapable of tracing pirate-decoders that contain keys based on a superlogarithmic number of traitor keys. We then combine the above condition with specific properties of concrete systems. We show that the Boneh-Franklin (BF) scheme as well as the Kurosawa-Desmedt scheme have no black-box tracing capability in the self-protecting model when the number of traitors is superlogarithmic, unless the ciphertext size is as large as in a trivial system, namely linear in the number of users. This partially settles in the negative the open problem of Boneh and Franklin regarding the general black-box traceability of the BF scheme: at least for the case of superlogarithmic traitors. Our negative result does not apply to the Chor-Fiat-Naor (CFN) scheme (which, in fact, allows tracing in our self-protecting model); this separates CFN black-box traceability from that of BF. We also investigate a weaker form of black-box tracing called single-query "black-box confirmation." We show that, when suspicion is modeled as a confidence weight (which biases the uniform distribution of traitors), such single-query confirmation is essentially not possible against a self-protecting pirate-decoder that contains keys based on a superlogarithmic number of traitor keys.

/pdf/self-protecting-pirates-and-black-box-traitor-tracing-352l9siaif.pdf

Self Protecting Pirates and Black-Box Traitor Tracing

We present a new generic black-box traitor tracing model in which the pirate-decoder employs a self-protection technique. This mechanism is simple, easy to implement in any (software or hardware) device and is a natural way by which a pirate (an adversary) which is black-box accessible, may try to evade detection. We present a necessary combinatorial condition for black-box traitor tracing of self-protecting devices. We constructively prove that any system that fails this condition, is incapable of tracing pirate-decoders that contain keys based on a superlogarithmic number of traitor keys. We then combine the above condition with specific properties of concrete systems. We show that the Boneh-Franklin (BF) scheme as well as the Kurosawa-Desmedt scheme have no black-box tracing capability in the self-protecting model when the number of traitors is superlogarithmic, unless the ciphertext size is as large as in a trivial system, namely linear in the number of users. This partially settles in the negative the open problem of Boneh and Franklin regarding the general black-box traceability of the BF scheme: at least for the case of superlogarithmic traitors. Our negative result does not apply to the Chor-Fiat-Naor (CFN) scheme (which, in fact, allows tracing in our self-protecting model); this separates CFN black-box traceability from that of BF. We also investigate a weaker form of black-box tracing called single-query black-box confirmation. We show that, when suspicion is modeled as a confidence weight (which biases the uniform distribution of traitors), such single-query confirmation is essentially not possible against a self-protecting pirate-decoder that contains keys based on a superlogarithmic number of traitor keys.

Self protecting pirates and black-box traitor tracing

An important open problem in the area of Traitor Tracing is designing a scheme with constant expansion of the size of keys (users' keys and the encryption key) and of the size of ciphertexts with respect to the size of the plaintext. This problem is known from the introduction of Traitor Tracing byChor, Fiat and Naor. We refer to such schemes as traitor tracing with constant transmission rate. Here we present a general methodologyand two protocol constructions that result in the first two public-keytraitor tracing schemes with constant transmission rate in settings where plaintexts can be calibrated to be sufficientlylarge. Our starting point is the notion of "copyrighted function" which was presented byNaccac he, Shamir and Stern. We first solve the open problem of discrete-log-based and public-key-based "copyrighted function." Then, we observe the simple yet crucial relation between (public-key) copyrighted encryption and (public-key) traitor tracing, which we exploit byin troducing a generic design paradigm for designing constant transmission rate traitor tracing schemes based on copyrighted encryption functions. Our first scheme achieves the same expansion efficiency as regular ElGamal encryption. The second scheme introduces only a slightlylarger (constant) overhead, however, it additionallyac hieves efficient black-box traitor tracing (against any pirate construction).

/pdf/traitor-tracing-with-constant-transmission-rate-4azb616z9v.pdf

Traitor Tracing with Constant Transmission Rate

An important open problem in the area of Traitor Tracing is designing a scheme with constant expansion of the size of keys (users' keys and the encryption key) and of the size of ciphertexts with respect to the size of the plaintext. This problem is known from the introduction of Traitor Tracing by Chor, Fiat and Naor. We refer to such schemes as traitor tracing with constant transmission rate. Here we present a general methodology and two protocol constructions that result in the first two public-key traitor tracing schemes with constant transmission rate in settings where plaintexts can be calibrated to be sufficiently large. Our starting point is the notion of copyrighted function which was presented by Naccache, Shamir and Stern. We first solve the open problem of discrete-log-based and public-key-based copyrighted function. Then, we observe the simple yet crucial relation between (public-key) copyrighted encryption and (public-key) traitor tracing, which we exploit by introducing a generic design paradigm for designing constant transmission rate traitor tracing schemes based on copyrighted encryption functions. Our first scheme achieves the same expansion efficiency as regular ElGamal encryption. The second scheme introduces only a slightly larger (constant) overhead, however, it additionally achieves efficient black-box traitor tracing (against any pirate construction).

Moti Yung

Papers

Self Protecting Pirates and Black-Box Traitor Tracing

Self protecting pirates and black-box traitor tracing

Traitor Tracing with Constant Transmission Rate

Traitor Tracing with constant transmission rate

Public Key Cryptography - Pkc 2006