Income tax 2022-2023, depreciation, office, home, land, exempted property, and basis adjustment. Let's do some wealth preservation with some tax preparation. Support Accounting Instruction by clicking the link below giving you a free month membership to all of the content on our website broken out by category further broken out by course. Each course then organized in a logical reasonable fashion making it much more easy to find what you need than can be done on a YouTube page. We also include added resources such as Excel practice problems, PDF files, and more like QuickBooks backup files when applicable. So once again, click the link below for a free month membership to our website and all the content on it. Most of this information comes from publication 946 how to depreciate property tax year 2022. You can find on the IRS website irs.gov, irs.gov. Looking at the income tax formula we're focused on line one income. Remember in the first half of the income tax formula is in essence an income statement but just an outline of scaffolding other forms and schedules flowing into these line items including the Schedule C basically an income statement in and of itself business income minus business expenses given us net business income which would flow into line one income here of the income tax formula. The first page of the form 1040 noting that the schedule C would flow into the schedule one flowing into the first page of form 1040 line number eight. The schedule C is a profit or loss from business form having an income statement format of income minus expenses. We're focusing this time on the expenses specifically on the depreciation. Remembering that even if you're in a cash based system, if you have large purchases, you're going to have to deviate from the cash based system. Why? Because the tax code forces you to generally to put them on the books as an asset which you can't see in the tax return because we only have an income statement if you're talking about a schedule C. But we're going to be able to depreciate them using the depreciation schedules allocating the cost over the useful life in accordance with the matching principle and a cruel concept. Although the tax code can get crazy and start deviating from that concept by using accelerated depreciation methods 179 special depreciation and so forth. All right, that given let's talk about an office in the home. We've got an office in the home. If you use part of your home as an office, you may be able to deduct depreciation on that part based on its business use. So meaning now we have a home. We've got an office in the home. That's a business part of the home. The question then is going to be, do I get to deduct the home office? So there's multiple things related to the deduction of the home office because you might have the utility bill that's paid for the entire home. You might have other, you know, water bill and so on that's paid for the entire home. But you might be able to allocate a portion of it to the office. You would think because it would be an ordinary and necessary business expense if you're allowed to have the home office. The other portion of that that might be applicable is depreciation because normally the home is a personal asset and for our income tax normal tax standards. Then we would think the only thing you'd get to deduct normally are those expenses that you incurred in order to generate revenue. There's a lot of exceptions to that general rule. When we think about a schedule A, for example, where we might be able to deduct the home mortgage interest and things like that, the property taxes and things like charitable deductions. Those are all kind of weird type of deductions that are deviations from the general rule of deducting something that was consumed in order to generate revenue. But the personal home is personal. So you would think in general you wouldn't be able to deduct the depreciation related to it. But if you use the home as an office, you would think you would get a portion of that depreciation as depreciation expense. So right here we're focused on the depreciation expense itself as part of in dovetailing on to that concept of a home office. Notice that if someone was renting a home, then the rent itself that they're paying might be part of the home office deduction. They wouldn't have depreciation. It's only if they owned the home that then you've got this depreciation that might add into or factor into this home office kind of situation. So for more information about depreciating your home office, you could see publication 587. So what property cannot be depreciated certain property cannot be depreciation. This includes land and certain exempted property. So normally when we think about things that are going to be depreciated, we're putting them on the books as an asset as opposed to expensing them because they're going to be a long lived type of items. And we want to allocate the cost to the period that it's being consumed. However, when we think about land, it's not being consumed in human lifetimes because the land doesn't deteriorate as the building that sits on the land does as does every other kind of thing that we buy for business. Typically like equipment, for example, furniture and so on. So terms you may need to know the glossary. We've got amortization similar to depreciation amortizing over time a basis that's kind of like the cost in essence of the property. Goodwill. This is an intention type of intangible concept intangible property intangible property can't touch it. It still has value, but it's intangible because you can't touch it remainder interest and term interest. Okay, given that, let's jump into it land. You cannot depreciate the cost of land because land does not wear out, become obsolete or get used up. So although we are using the land to help generate revenue, the land hasn't been consumed. It's not going away to help generate revenue. And that's kind of the argument as to why we're not going to allocate the cost of land over the useful life. Because in theory, the land's useful life is beyond our human useful life. So the cost of land generally includes the cost of clearing, grading, planting and landscaping. So now you get into the issue of what is land versus building type of issue, which can be kind of confusing. Because what if I bought a piece of land with a building on it? Well, if you plan on using the building, then you're going to have to basically allocate between the land and the building. But if you plan on just clearing the building off the land, I bought the land for the land. And there happened to be a building on the surface of my land. Then if you just tear down the building, then the building isn't what you wanted. So that might be as part of the cost of the land in that case. That's part of your cost possibly. So you get a little bit of unusualness in that. So once again, the cost of land generally includes the cost of clearing, grading, planting and landscaping. Although you cannot depreciate land, you can depreciate certain land proportion costs, preparation costs, such as landscaping costs incurred in preparing land for business use. So when you're tending to the land, then the question is, well, obviously you're not really improving the land as just a piece of dirt that's going to be there for some time. You would expect maybe you'd be able to expense the costs of landscaping and so on, which aren't things that are going to be eternal. Eternal love and eternal wealth. As the piece of rock that's below the land is, at least for human lifetimes, you would think. These costs must be so closely associated with other depreciable property that you can determine a life for them along with the life of the associated property in that case. You can determine the life of the property if you're going to be depreciating the property, which is one of the requirements for depreciation. Example, you constructed a new building for use in your business and paid for grading, clearing, seeding, and planting bushes and trees. Some of the bushes and trees were planted right next to the building while others were planted around the outer border of the lot. So if you replaced the building, you would have to destroy the bushes and trees right next to it. So these bushes and trees are closely associated with the building, so they have a determinable useful life. So notice how you see how that kind of works. They're saying, well, they're trees and bushes. That's part of the land, you would think, generally. But because they're right next to the building and you'd have to tear them out, if you destroyed the building, then maybe you can have them have a depreciable life, which would generally be better than having to call them just land because then you might be able to get the expense of depreciation related to them. Therefore, you can depreciate them, add your other land preparation costs to the basis of your land because they have no determinable life and cannot be depreciated and you cannot depreciate them. Okay, accepted property. Even if the requirements explained in the preceding discussion are met, you cannot depreciate the following property. You've got property placed and serviced and disposed of in the same year. So you put it in there, it's supposed to be a long-term type of thing. If you disposed of it in the same year, it doesn't seem like it's a long-term thing. That's something funny happened in that case. So determining when property is placed and serviced is explained later. Equipment used to build capital improvements. So you must add otherwise allowable depreciation on the equipment during the period of construction. So basis construction to the basis of your improvements. See uniform capitalization rules. That's in publication 551. Section 197 intangibles. You must amortize these costs. Section 197 intangibles are discussed in detail in chapter 8 of publication 535. If you want to dive into them in more detail, intangible properties such as certain computer software that is not section 197 intangible property can be depreciated if it meets certain requirements. See intangible property later. So intangible property being property that has value, but it's not tangible, not something you could touch or kick or whatever, then can get a little bit more confusing, right? So now you're dealing with these intangible properties, some of which might have value to them due to like laws, for example. So certain term interests. Okay. So certain term interest in property. You cannot depreciate a term interest in property created or acquired after July 27 1989 for any period during which the remainder interest is held directly or indirectly by a person related to you. A term interest in property means a life interest in property and interest in property for a term of years or an income interest in trust. So now you've got, when you're thinking about property and the ownership of the property, you've got a bit of a confusion in terms of ownership of the property for a term kind of situation. So that kind of muddies up the waters in terms of what does it mean for ownership and therefore the related depreciation rules. All right. Basis, that's more of an unusual situation, of course. Basis adjustments. So if you would be allowed a depreciation deduction for a term interest in property, except that the holder of the remainder interest is related to you, you must generally reduce your basis in the term interest by any depreciation or amortization not allowed. If you held the remainder interest, you must generally increase your basis in that interest by the depreciation not allowed to the term interest holder. However, do not increase your basis for depreciation not allowed for periods during which either of the following situations apply. The term interest is held by an organization exempt from tax. The term interest is held by a non resident alien individual or foreign corporation and the income from the term interest is not effectively connected with the conduct of a trade or business in the United States. Exceptions. The above rules do not apply to the holder of a term interest and property acquired by gift, bequest or inheritance. They also do not apply to the holder of dividend rights that were separated from any stripped preferred stock if the rights were purchased after April 30, 1993, or to a person whose basis in the stock is determined by reference to the basis in the hands of the purchaser.


Aaron, between seven pitchers being needed tonight, the way everything unfolded in that 10th inning and the game ending on a wild pitch, what did it take to squeeze out a win tonight? Couple wild pitches there. But no, a good job getting behind the eight ball there a little bit, obviously, early down four, nothing, a big inning to get us back in it, and they fought right back. Big homer, but not perfect. The season's not going to be that way, so just glad we were able to continue to grind and grind. Bullpen came in and did a really nice job for us to give us a chance, and we're able to outlast them tonight. Luis Severino obviously left early with shoulder tightness. What was the first sign for you and your coaches in terms of seeing what was wrong? Well, a little bit, the velocity down. Then when I talked to him after the first inning, he's just like a little leery of letting it go, but he kept saying, I feel really good. Something was telling the hold back a little bit, so that was the biggest thing. Sometimes he can be that way a little bit early, where he's kind of, he'll kind of ease into it a little bit, and I think in his mind with his injury history, he gets a little cautious, but the whole time he was saying, I feel really good, but then there's nothing there from a tightness standpoint, and I said, if you feel anything, you gotta, and he was dead set on that, so I'm glad he did that, and we'll see what we got tomorrow. What do you think of Clay's performance coming off last night? Really good, I mean, walks the first batter, and obviously struggling a little bit with his command, again there early, and frankly, facing a little bit of adversity there, right? It comes in all shapes and sizes throughout the year. I don't care who you are, best reliever in the league. The last couple nights have been a grind for him, and he kind of figured it out and grinded his way through it, made some big pitches, got us back in the dugout, so just proud of that effort, and knowing that you're staring a little bit of adversity out there trying to find it, and he did. I know it could be a little premature, but with Severino's situation, does that change the weak plans for Domingo-Herman yet? Not necessarily, but that means possible. We'll start to have those conversations, kind of try and get our arms around what we have with Sevi, and then we'll go from there. Andy, and Brian. The way you describe it, it sort of sounds like he just couldn't get loose, rather than any pain, and just didn't persist it for the... Yeah, I think so, yeah.


All right, thanks and thanks to everybody for showing up so early of course in the morning although I saw that there was coffee outside So you're expected to pay attention Okay, so group-based secure computation. Let me start by saying what do I mean by this? Okay, so here's sort of a philosophical look of the landscape of secure computation approaches so in one kind of category I consider these classical approaches of things that we we typically talk about things like yow and GMW based protocols and and So these have been around for decades and have really been fine-tuned and optimized in a lot of fantastic ways a lot of practical systems But one of the downsides of these is that inherently both of these approaches can everything in this category inherently requires Communication to grow with the entire circuit size of that you want to do and they compute Okay, so in comparison with these In 2009 kind of these breakthrough works in fully homomorphic encryption. We found the first ways of Getting secure computation where the the communication grows asymptotically not with the circuit size, but just with the Essentially the input and output size as you would want So this is fantastic in terms of asymptotic costs. Unfortunately, there's a lot of downsides If you actually look at some of the concrete costs here, so this has been around since 2009 There's been tremendous work and actually tremendous progress in improve optimizations, but still essentially what exists now is even kind of more expensive in Essentially all the okay. I'm not even going to go into the extremes that let's just say there's much to be desired Okay, and one of the the negatives that kind of contributes to this not only from a theoretical standpoint But also in terms of practicality is that all of these FHE based Constructions so despite a lot of work in trying to get it based on different assumptions Essentially everything relies on the same narrow window of these noisy encodings based on lattices So one of the one of the downsides of that is So as I mentioned not not only in terms of we'd like a wider spectrum of things But because there's a lot of these generic lattice attacks That means that you actually have to crank up kind of the security parameters and this this contributes also to poor poor efficiency Okay, so now kind of shifting to the topic of this talk It's going to be kind of a different approach to to secure computation And this is kind of one of the things that we introduced last year in the crypto result Based on what's known as homomorphic secret sharing okay, so this is the topic of this talk and In the title of the talk we refer to this as group based secure computation and sort of the reason for this is that for the first time Aside from these lattice based constructions. We have constructions of homomorphic secret sharing Based on the discrete log type assumptions, and I'll talk a lot about this today Okay, so what is this homomorphic secret sharing and this is going to be kind of the central friend of ours today Okay, so this is an extension of standard secret sharing You think about some secret X split into two different parts where we require standard security So if you just see one of these two different shares, you don't know what the secret X is Okay, but the homomorphic aspect is that I can locally compute on a single share in a homomorphic fashion So say that there's some program P that we'd like to evaluate Such that these is that these outcomes here have an additive reconstruction Okay, so what does it mean again? I have share X 0 you have share X 1 we locally evaluate and these add together Okay, so an interesting relaxation That's going to be important for today is what we refer to as Delta HSS And this is essentially homomorphic secret sharing but with some sort of probability of error Delta And this probability is taken over the the randomness of the share procedure Okay, and in turn so we have this probability Delta in error and we allow the runtime evaluation The evaluation run time to scale also with one over Delta. So for example, we're talking about one over one over a polynomial probability here Okay, so in the crypto work from last year We showed essentially that if you have so two different things first of all we gave a construction of Delta HSS So go into more detail later But also we showed that if you have even Delta HSS this is enough to give you say succinct two-party secure computation and roughly what the framework is is think about these two parties with some secret inputs We can run a small size and generic say secure protocol in order to generate these homomorphic secret shares And the bulk of Sort of the work is going to be done in the locally with homomorphic evaluation Okay, in the end we can do some sort of recombination protocol to get the final answer Okay, so the communication here. I don't have to communicate with respect to the circuit size But just with respect to input and output size Okay Good. So yes, so this has two different results the construction and this this putting it together Okay, so This this framework is very interesting particularly from a theoretical aspect We can do things like beat the circuit size barrier of communication But this is completely Like a basically as it sat before it was a very interesting direction but completely theoretical and in this work We try to make strides in a couple different dimensions of pushing not only kind of seeing what is the best that we can do Asymptotically in a theoretical direction But also can we push this can we get this to something that can really be practical and I think that there's a lot of Exciting stuff to be said in that direction Okay, so we look in particular at three different dimensions of improvement in secure computation here The round complexity the communication and computation So the first result I am going to say it's completely theoretical But basically we show that we can get a two-round multi-party computation from DDH with a little bit of setup Okay, and previously this this Result was only known based on learning with errors these lattice assumptions or indistinguishability obfuscation Okay, next in kind of a different direction We we look at improvements at the communication complexity So first of all kind of trying to shave off these poly and the security parameter factors We can really get this communication to just input plus essentially output size and an additive poly lambda And as one corollary of this and so if you consider the the specific functionality of trying to generate one bit ot's oblivious transfer Then we can actually achieve this So if you have n of these with total communication scaling essentially four times n with a low-order term Okay, and previously if you wanted to get any sort of constant overhead times n This was only known based on poly stretch local pseudo random generator assumptions or fly hiding And even in these cases the constant was significantly larger Okay, and the third category is is really kind of treading forward into the the direction of Practicality and we we have a lot of different Optimizations and that when you compare at least to to the crypto result. We're talking orders of magnitude improvement Okay, good, so I'm going to to try to give you a little bit of a flavor of each of these different directions today But to do it in order to do that I'm going to start with a little bit of a crash course of the crypto paper Okay, so there with me here So in the crypto paper and we gave this construction based on DDH for Delta HSS for the class of branching programs And essentially how we do that is we show how to support two different homomorphic evaluation procedures Okay, so the first is just addition of two values And the second is going to be a restricted form of multiplication I'm sometimes going to refer to this as RMS and multiplication. This is restricted multiplication straight line for the class of programs And what does this allow this allows me to multiply any of my any intermediate computation value V Times an input. Okay, so this is not general circuits because it doesn't allow me to multiply two intermediate computation values But these together will give me enough for for branching programs Okay, ignore this arrow here All right, so at a high level I'm think about three ways to encode a zq element Okay, so what is zq here? We're talking about DDH So let G be some DDH hard group of order prime order q and little G will be a generator everywhere Okay, so first type of encoding is just going to be taking this value you and raising it as the exponent second second is a standard additive secret sharing Over zq and the third is going to be a form where essentially so it'll be a pair of group elements shares Such that the group elements differ in discrete log by the value that's in here. Okay Okay So first observations about these encodings Within any one of these levels if I stay within the same level I have an additive homomorphism property Okay, you can convince yourself of that But the second and the special thing that's going to be helpful for us is that there's a natural pairing procedure Okay, so suppose that I have some level one encoding have g to the u both of us do and that we have across us Additive secret shares of some value V Okay, then if each of us takes g to the u and raises it by our share of V This gives me exactly this type of level three encoding of the product. Okay, so essentially the discrete log differs by the product UV Okay, so now let's see how we're gonna get secret sharing with homomorphic So think about kind of roughly suppose it as part of the secret shares of a value I give you level one and level two encodings of my inputs Okay, and now for the homomorphic evaluation We're going to maintain the invariant that for any partial computation value We'll hold additive shares of this value across one another Okay, so first of all this holds for the inputs Now if we want to perform an addition this follows directly by the additive homomorphism of these additive shares And now let's see how to do an RMS multiplication as you may suspect. There's gonna be something fishy going on here So so suppose now what do I have? By the invariant if I have this partial computation value We're holding additive secret shares of this value and In addition each of the inputs I gave you this extra helper information. This was part of the initial Secret sharing itself which was basically g to the u. Okay, so using this pairing procedure We can get to these multiplicative this level three shares of the product and The question is how do I get back to additive shares which we need in order to maintain the invariant to get to the next step Okay, and this that on is the share conversion procedure Which is really one of the kind of interesting and a technical and mysterious parts of this work So essentially if I think about taking this cyclic group g and Flattening it out in terms of different steps of multiplying by the generator So the fact that we have these shares With a differ in the discrete log by the by some payload in this case the Z is UV Means that you have some group element. I have some group element that differ on this this position here by this payload So how we do the conversion? We say ahead of time. Let's share Let's say both have agree upon some random sprinkling of special points these red points here of the desired density delta Okay, so now once we get these shares each of us will just output The distance from whatever the share is in terms of multiplications by the generator to the first special point that you hit Okay, so as long as there's no special point between us this will give us exactly what we want Okay, so here this distance in this distance will differ exactly by this gap zine Okay, so aside from this error probability of delta that we have something inside of here and then we'll get a correct conversion Okay, so I'm hiding certainly a number of things under the rug about this construction in particular It's certainly not okay for me to give you g to the you for some secret value you So what we do in reality good is So this g to the you is going to be replaced by an El Gamal encryption Actually of you and there's a little bit of machinery that needs to be done to modify this pairing Okay, but a lot of the basic structure is exactly is here and kind of one of the takeaways is that this share conversion We're going to have to run it for every multiplication or a miss multiplication and you have to run it for where this gap is the value the multiplied value and Also once for each of the bits of the the El Gamal secret key times that value Okay, so I'll leave it as a mystery why but please ask me afterward if you're interested Okay, so this is a takeaway for later Good so so moving forward Our first result is to get two round multi-party computation from DDH. So a little bit of context And so certainly two rounds at least two rounds is known to be necessary for secure computation And if you're interested just in the regime of two parties Then actually there's a lot of sort of simple things that you can do to get to two rounds of of interaction And the question becomes interesting when you're looking at the multi-party setting. Okay, and so this is something That we didn't even really know about until much much more recently and there's been a sequence of works achieving two round computation under various assumptions, so LWE and IO and here this is sort of an improvement of setup So in this work We we get something that's a little bit weaker than these the more recent versions here The difference is this is a common random string versus we require public key infrastructure And also we have a limitation on the number a constant number of parties But the punch line here is that even when you look at these restrictions anything here with two rounds and Was not known at all under DDH. So this is what we do Okay, so for the starting point of this construction I want to consider a little bit of a generalized version of the the two-party secure computation slide that you saw from before So remember before there was this phase where we're two people were running some sort of secure protocol to get shares of the inputs Now I want to think about Since we need to get to the multi-party setting. I'm going to consider this client server model Okay, so in the same way we can if we had all these clients who actually have inputs Let's temporarily assume that we have two servers to help us Okay, so so first we'll run some sort of protocol to get these shares The servers will do a homomorphic evaluation So I want to put a little little bit of a side note here So if you can homomorphically evaluate the program that you're interested in directly Then that's fantastic at this point. That would mean if you can if you're interested in computing a branching program But even for general circuits This is not a problem because we can essentially use standard tricks of instead homomorphically evaluating Randomized encoding so you could think of it basically if I have some a very deep circuit I can squash this down into Yau garbling for example of the circuit Okay, so say we have this homomorphic evaluation and recall that this gives us the property that aside from some error parameter Delta That these shares will add to the correct output Okay, so you can deal with correctness in terms of Like iterating this many times Okay, but if you if you stop right here Then this would actually give you a security issue and the reason is that if I exchange these shares So some of them have errors and the error is going to be dependent on the inputs Okay, so because of that you have to sort of clean up and hide where the errors occurred So there's an additional protocol to output the correct value Okay, so what do we have in terms of rounds? So oops Direction so we have constant number of rounds, but it's sort of a large constant So to get to two rounds, there's three primary steps So first we have to clean up this first step to make it so that you can run just in a single round And this is where the public key infrastructure is going to take place in Step two we're going to remove the need for this majority this extra MPC by making it So it's safe to just exchange the shares even though there's going to reveal where the errors occurred Okay, and step three is going to be just to support not just two servers You can essentially think about this client server model as is a setting multi-party computation But you're assuming that two of the parties at least one of those two is honest So we need to extend this to more servers Okay, so I'm not going to talk about step one or step two at all Basically step one really uses homomorphisms of El Gamal in a careful way and step three You can execute with more servers by using sort of standard server emulation tricks Okay, so for step two to give you a little bit of a flavor and Again, the issue is that I can't just exchange the the homomorphic evaluated shares Because there'll be errors and this leaks information on the inputs and the the input So what is it that it actually leaks? Well, remember the probability of error occurred if there was a red point between us Okay, but for example if we're secret sharing a value of zero then there is no space between us so exactly what this is going to correspond to is The the error is directly of a function of this intermediate computation value and we can show that essentially So this this is exactly the sort of setting Where we have solutions for leakage resilient Circuit compilers. Okay, so basically instead of evaluating the standard circuit itself I'm going to evaluate one that's leakage resilient with respect to partial information leakage Okay, so this is kind of the The first part and the real challenge or an additional challenge So if you remember from a takeaway slide I not only run this share conversion procedure on the partial computation values But also on the bits of this El Gamal secret key times the computation values Okay So we have to address this leakage as well and we do so by kind of adding these additional randomization and secret sharing tricks Okay, and so again, I'm just trying to kind of give you a flavor of each of these directions so in the second direction for optimizing communication and I want to give you kind of a little clean I think it's a bit of a cute takeaway here The notion of punctured OT. Okay, so for standard oblivious transfer You think about some sort of large database and the receivers interested say in one position in the database But what if you have a receiver who is interested in almost all of the positions of the database? Okay, so we give As one of our techniques we give basically a cheap protocol for achieving this almost all oblivious transfer via punctured PRF's punctured pseudorandom functions and The protocol is actually quite simple. Basically, you can run a generic procedure generic NPC Where the output is that the server receives some pseudorandom function key and the receiver will receive a key That's punctured in the positions that he's not supposed to learn Okay, so now given this information the sender can mask each of these values by the PRF evaluations send them over and The receiver will be able to reconstruct exactly those positions that were not punctured out Okay, so we use this and this this piece as a way of dealing with with leakage in a cheaper way Okay, so in the previous I told you kind of this blow-up of leakage resilient Compilers and everything but if I allow you to communicate a little bit more via this process You can get this much more efficient Okay, so this is together with a couple additional tricks that we have to introduce Okay, so finally I want to give you a flavor of some of the the concrete Optimizations that we have so as the baseline, what is the cost of this actual homomorphic evaluation? So the cost really comes from multiplication and there's two different parts to this The first was this pairing procedure I mentioned which will correspond to essentially Exponentiations and a product over the group Okay, and the second part is the share conversion procedure. So as I've described basically Order of one over Delta kind of an expectation times you have to do this multiplication by a generator and testing if this is a special point So let me describe some of the the optimizations that we have The first is to consider specific what we refer to as conversion friendly groups as a zp star where Two is a generator of the group and the prime here that you're modding by is very close to a power of two Okay, so what does this give me this gives me that each time that I need to do this Multiplication I can essentially shift and then do a small addition Okay, the second big optimization that we have is redefining. What are the distinguished points? What are these red points and how are they defined? Okay, so I'm not going to mention we have a provable optimization that de-randomizes But a heuristic way of looking at this is well instead of saying it say Some sort of pseudo random function evaluates to a special value What if I just say this point to this group element is special if in a certain window of its bit representation? I have all zeros Okay, and this if you do this this allows a lot of Amortization and huge improvements in terms of testing the speed of testing Okay, and so ultimately you can get this to to an average of even less than one machine word per Of these stepping along multiplication Okay, so I'm kind of a high level there's tons of other optimizations. We've considered And the bottom line here is that ultimately it seems like compared to FHE one of the things that we have is that the size Is quite promising and the bottom line in terms of so this was first kind of done by by estimates in and now In we have a follow-up work actually that's starting to look at implementations So ultimately for this RMS homomorphic multiplications if you want error probability Delta It seems that you can get about 200,000 times Delta multiplications per second Okay, so summing up and we have these three different types of results It kind of going from from theoretical down to practical as you go along And I want to end with a couple open questions I think really this is kind of a fascinating area for me and in general So first of all there's a lot of questions even just about this component this homomorphic secret sharing Is there any way that you can go beyond branching programs safe from from DDH? So we know from learning with errors that you can get HSS, but it ends up basically going through fully homomorphic encryption Are there so all of the kind of all the solutions that I've really talked about are for the two-party setting So not for the for the MPC you can extend this to multiple parties But when you're just looking at say the homomorphic secret sharing tool itself as soon as you go from two to three parties We know very little. It's kind of embarrassing. How big the gap is And in particular this is because this conversion share conversion procedure really relies on the fact that there's two parties Can you get this from this sort of thing from other assumptions? For the share conversion, can you get a better error versus run runtime trade-off right now? It's it's directly inverse proportional It's not clear if you can do better Can you try to do some sort of better fault tolerance at the program level? Looking at this two-round MPC from from DDH Can you remove some of the restrictions that we have so for example polynomial number of parties and Removing the the public key infrastructure And I didn't really talk about it, but I kind of want to push the agenda since I'm here So I talked mostly about this kind of high-end HSS They can support say branching programs and uses public key sorts of tools, but you can actually we looked a little bit at some more low-end HSS versions and turns out you can get some really interesting function classes based just on one-way functions and So you can get a lot of there's been some interesting works with with applications building on top of these Okay, so there's a lot of questions here. Can you what can you do? How far can you go and and so on all right, so with this I will conclude


Live from Orlando, Florida, it's theCUBE, covering Microsoft Ignite, brought to you by Cohesity, and theCUBE's ecosystem partners. Welcome back everyone to day two of theCUBE's live coverage of Microsoft Ignite. We are coming at you from the Orange County Civic Center in Orlando, Florida. I'm your host, Rebecca Knight, along with my co-host Stu Miniman. We're joined by Patrick Moorhead. He is the founder and president and principal analyst at Moor Insights and Strategy. Thank you so much for coming back on theCUBE. You're an esteemed CUBE alum, so. Gosh, this is great. Can you introduce me on every show, please? I would be happy to, delighted. So Patrick, before the cameras were rolling, we were talking about how many, frankly, tech shows you go to a year, we said 40, 45. That's about right.  It's kind of like you do, right? Right, sure, sure, yeah. So this is your 10th time at an Ignite, or an Ignite-like show, it used to be called TechEd. So what are your first sort of quick takes on what this conference, what you're seeing, what you're hearing? Yeah, so Microsoft has three layers, like a three-layer cake to their events. You have developers, you have customers, and you have channel. And this is their customer event. So what might seem like rehash or maybe build or inspire is you have customers who haven't heard this content before. So it's really about getting them engaged and things like that. And what we've heard first and foremost is we had 45 Azure announcements, but I think the biggest news was about the open data initiative that, I mean, how often do you have the three CEOs up on stage of where most corporate data sits with Microsoft, SAP, and Adobe? So it was impressive. And that's probably the number one thing so far. Okay, so let's dissect that a little bit. What are your thoughts? I mean, we're sort of questioning, it's a big idea. Right. When will customers actually see the benefit and is there a benefit to customers? So when I look at these big corporate announcements, I'm thinking, okay, is this thing paper? Is this thing real? How far does it go? I think this is real. When I dug under the covers in some NDA things that I can't give details on, there was meat there for sure. But where this all starts is two things are going on here. First of all, to do machine learning correctly, you have to have a lot of data, right? Yesterday's big data is today's machine learning. You have to have it all together. Now, you can pull in disparate data sources into your enterprise and work on that data, but it takes a lot of cleansing. Most of the time in machine learning is getting the data ready to be worked on. What having data interoperability standards means you can bring it in, you don't have to cleanse it as much, and you can do real-time analytics and machine learning on it. So it's agreement that says, we're all going to come in. If it's customer data, it's going to look like this with different fields. You would think something like XML could do this, but this is bigger. And from a competitive standpoint, I have to ask the big question, where's Salesforce and where's Oracle, right? They're the two odd companies out. Yeah, really interesting. You mentioned that there were a lot of Azure announcements here, something like 45. I was reading Corey Sanders had a blog of just, you know, listen, listen, list, and it's typical of what we see in the cloud. You and I, we go to AWS, reinvent, and it's like, oh, let's talk about all the compute instances, all the cool news storage, all the things, there's cheering and everything for every micro and macro, the thing that happens there. But are there any things that jumped out at you? We had Jeffery Snover on the program yesterday. We talked about the data boxes, like the edge and the various versions of those. Those seem kind of interesting when we talk about data and movement, but anything in kind of the Azure space that kind of got your attention. Yeah, so aside from the data boxes, I was really excited about AutoML. So, three ways you can do ML. You can do everything from scratch. You can take an off-the-shelf API, and then you can do something in the middle, which says, kind of like the free bears, right in the middle. Google at GCP announced something like this and so did Azure. And essentially what this is is it auto-tags your data. It's smart enough to know that this is an image as opposed to you having to start at the very beginning and hand-code some data. And it's not automatic because the key, so a good example might be an audio machine learning algorithm where you might need it for an airplane, versus a car, versus the factory floor, versus a smartphone application. Those are all different environments and your algorithm's going to be different. But as an enterprise, you might not have the PhD on staff to be able to do that, but you can't live with the off-the-shelf API. There's another thing that kind of struck me, a little bit of dissonance I saw there.  Microsoft has gotten into hardware in a lot of places. When they talked about their IoT piece, they were like, we're going to put things out on the edge. And then on the other stream, it's like, well, but they're open and it's APIs and developers and software and not only Adobe and SAP, but now with Red Hat, talking about all they're doing with Linux, how do you reconcile the, I've heard people from Microsoft, we want to completely vertically integrate the stack and that's not something that I hear from the Googles and Amazons of the world. It was, I thought we're kind of past that. No one company can do it all. And the other hand, they're very open and give you a choice. How do you look at those things? So this all stems with the slowdown of Moore's law for general CPU compute. So as Moore's law is slowing down, we need to throw different kind of accelerators at the same problem to keep innovation going up and to the right at an increasingly faster pace. So people have gone to GPUs and CPUs and almost every one of the big infrastructure players has done that, right? Whether it's Google, Apple, AWS, that they all have their own hardware. Part of it is to accelerate time to market. The other is to get a lock in. I'm still trying to figure out which one this is. Now, Microsoft is saying very clearly in Azure IoT Edge that you can send your data even if you have their hardware to AWS and NGCP. And I think enterprises are going to take a quick look and I've been doing this almost 30 years. The gray hair, I have gray hairs to show for it, but you just have to pick your lock in, right? Enterprise IT always gets locked in and the question is what you lock in on. If you go with Oracle and then build applications around it, you're locked into Oracle. If you go with a certain hardware OEM, you could be locked into a certain OEM with converged infrastructure. So I think it's just picking the poison. You're going to have some people who are very comfortable with going all Microsoft and you'll have some people who want to piece part it together and look to the future. We still have people who were brought up on mainframes and they don't want to be there. They want to have flexibility and fluidity. One of the things you were talking about with the slowdown of Moore's Law, Microsoft, and frankly every other technology giant is really trying to stay ahead of the innovation curve. Microsoft, 42 years old, a middle-aged company and really in the tech world, a really old company, is Microsoft effective at this? I mean, do you see that this is a creative and ingenuitive and innovative company? Microsoft is one of the only companies that has been able to turn the corner from being aged and experienced, I guess like us, and moving into the new zone and everybody's work has had to do that. As an analyst used to, I remember getting Gartner and IDC reports on paper, right? But now it's very different. We're up here on theCUBE, we're on Twitter, we're doing research reports, right? So everything is changing and Microsoft has had to change too. And five years ago, when Azure hadn't really taken off, they had a billion dollar write down on Surface hardware, bought Nokia, shut Nokia down. You were wondering, wait a second, what really is happening? But then Satya came in and to the company's credit, it has completely turned around. And I will state though, you know, there's difference between perception and reality. I think a lot of the things that Balmer had in place were absolutely the right things. I think Satya gets a lot of credit for it, but these things just didn't magically appear when Satya came in. So a lot of things they did were right and it was perceived to be new leadership and therefore they're looking good. Yeah, I love it because we had quite a few Microsoft people on the program and a lot of them, 10, 20 years with the company. And they said, it's still the vision we had, but one articulated really well, I said we're even more focused on the customer than ever and that gets me really excited. Yeah. I want to ask you, when people look at this show, such a broad ecosystem, so many different pieces, what will they be talking about later in the year? My initial take coming out of it is, I'm a little surprised that we're talking so much about things like Windows 2019 and the Office 365, Microsoft 365, Dynamic 365. Obviously it's Microsoft strength, it's where they've got the most customers, but are the operating still relevant in the future? I met with the program manager of Windows 29 servers last night, Erin, and she had said that they had 1,300 people, they had to turn away from the Windows 2019 server. And it was 4,000 people, right? And I flippantly said, oh my gosh, I didn't think Microsoft still did that, right? It's all as a service, but I was just kidding, of course, but I think that shows how long it takes for people to move, but I think what we'll be talking about in a year is, has Microsoft delivered on its IoT commitments in IoT Azure Central? How much of their business has moved to, I'll call it on-prem software in a box, to as a service, right? So, Dynamics 365, Office 365. And then finally, I think we're going to see the workflow and here's something that my head finally went ding on, is Microsoft's strategy to surround the data and then do workflow on it to supplant Oracle SAP applications around the data. That's what I think we'll be talking about in a year. Yeah, one other specific I wanted to see if you've got some data on, because it's something we wanted to understand Azure Stack. The press oligog on it for the last couple of years. I really haven't talked to, I've talked to the partners that are working and people like Intel, Lenovo and the like, that are doing it, I haven't talked to too many customers that have applied service providers, yes. But what are you hearing, what are you seeing it? Is Azure Stack a big deal or is it just one of the pieces in the multi-cloud data application strategy that Microsoft has? So, Azure Stack is a big deal and I think that it's getting, it was a slow boil, to be honest with you. I mean, the company changed hardware strategies. It was first an ODM model and then it went to an OEM model and a very narrow OEM model. The compute requirements to Azure Stack were too big to some people, so it's a slow boil, but I look at what has the competition done. Now, to be even a public cloud player, you have to have an on-prem capability. With Google, it's PKE on-prem. You have Greengrass and Amazon DB that's on-prem sitting on top of VMware. So, hybrid cloud, multi-cloud is a real thing. I just think it's getting a little bit slower start than everybody had thought. Great. Well, Patrick, thank you so much for your insights. These were terrific. It was great having you on the show. Thanks for having me. I'm Rebecca Knight for Stu Miniman. We will have more from theCUBE's live coverage of Microsoft Ignite in just a little bit.